https://github.com/zhaoxin94/awesome-domain-adaptation

A collection of AWESOME things about domian adaptation
https://github.com/zhaoxin94/awesome-domain-adaptation
adversarial-learning awesome-list domain-adaptation few-shot-learning image-translation optimal-transport paper transfer-learning zero-shot-learning
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A collection of AWESOME things about domian adaptation
Host: GitHub
URL: https://github.com/zhaoxin94/awesome-domain-adaptation
Owner: zhaoxin94
License: mit
Created: 2018-05-13T02:42:39.000Z (about 8 years ago)
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Last Synced: 2025-05-14T06:13:25.095Z (about 1 year ago)
Topics: adversarial-learning, awesome-list, domain-adaptation, few-shot-learning, image-translation, optimal-transport, paper, transfer-learning, zero-shot-learning
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- Readme: README.md
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Awesome Lists containing this project

awesome-interesting-topics-in-nlp - CV
awesome-situational-awareness - Awesome Domain Adaptation
Awesome-Paper-List - Domain Adaptation
awesome-machine-learning-resources - **[List - domain-adaptation?style=social) (Table of Contents)
awesome-list - zhaoxin94/awesome-domain-adaptation - A collection of AWESOME things about domian adaptation. (Machine Learning / JavaScript)
awesome-neural-adaptation-in-NLP - awesome-domain-adaptation
awesome-computer-vision - Awesome Domain Adaptation
ultimate-awesome - awesome-domain-adaptation - A collection of AWESOME things about domain adaptation. (Other Lists / TeX Lists)
awesome-of-awesome-ml - awesome-domain-adaptation (by zhaoxin94)
awesome-lists-machine-learning - Domain adaptation
jimsghstars - zhaoxin94/awesome-domain-adaptation - A collection of AWESOME things about domian adaptation (Others)
awesome-artificial-intelligence-research - Awesome Domain Adaptation - domain adaptation papers and code. (Core Machine Learning Research / Robustness, Interpretability, and Learning Paradigms)
README

          # awesome-domain-adaptation

[![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT) 

This repo is a collection of AWESOME things about domain adaptation, including papers, code, etc. Feel free to star and fork.

# Contents

- [awesome-domain-adaptation](#awesome-domain-adaptation)

- [Contents](#contents)

- [Papers](#papers)

  - [Survey](#survey)

  - [Theory](#theory)

  - [Explainable](#explainable)

  - [Unsupervised DA](#unsupervised-da)

    - [Adversarial Methods](#adversarial-methods)

    - [Distance-based Methods](#distance-based-methods)

    - [Information-based Methods](#information-based-methods)

    - [Optimal Transport](#optimal-transport)

    - [Incremental Methods](#incremental-methods)

    - [Semi-Supervised-Learning-Based Methods](#semi-supervised-learning-based-methods)

    - [Self-training-Based Methods](#self-training-based-methods)

    - [Self-Supervised Methods](#self-supervised-methods)

    - [Transformer-based Methods](#transformer-based-methods)

    - [Other Methods](#other-methods)

  - [Semi-supervised DA](#semi-supervised-da)

  - [Weakly-Supervised DA](#weakly-supervised-da)

  - [Zero-shot DA](#zero-shot-da)

  - [One-shot DA](#one-shot-da)

  - [Few-shot UDA](#few-shot-uda)

  - [Few-shot DA](#few-shot-da)

  - [Partial DA](#partial-da)

  - [Open Set DA](#open-set-da)

  - [Universal DA](#universal-da)

  - [Open Compound DA](#open-compound-da)

  - [Multi Source DA](#multi-source-da)

  - [Multi Target DA](#multi-target-da)

  - [Incremental DA](#incremental-da)

  - [Multi Step DA](#multi-step-da)

  - [Heterogeneous DA](#heterogeneous-da)

  - [Target-agnostic DA](#target-agnostic-da)

  - [Federated DA](#federated-da)

  - [Continuously Indexed DA](#continuously-indexed-da)

  - [Source Free DA](#source-free-da)

  - [Active DA](#active-da)

  - [Generalized Domain Adaptation](#generalized-domain-adaptation)

  - [Model Selection](#model-selection)

  - [Other Transfer Learning Paradigms](#other-transfer-learning-paradigms)

    - [Domain Generalization](#domain-generalization)

    - [Domain Randomization](#domain-randomization)

    - [Transfer Metric Learning](#transfer-metric-learning)

    - [Knowledge Transfer](#knowledge-transfer)

    - [Others](#others)

  - [Applications](#applications)

    - [Object Detection](#object-detection)

    - [Semantic Segmentation](#semantic-segmentation)

    - [Person Re-identification](#person-re-identification)

    - [Sim-to-Real Transfer](#sim-to-real-transfer)

    - [Video Domain Adaptation](#video-domain-adaptation)

    - [Medical Related](#medical-related)

    - [Monocular Depth Estimation](#monocular-depth-estimation)

    - [3D](#3d)

    - [Fine-Grained Domain](#fine-grained-domain)

    - [LiDAR](#lidar)

    - [Remote Sensing](#remote-sensing)

    - [Others](#others-1)

  - [Benchmarks](#benchmarks)

- [Library](#library)

- [Lectures and Tutorials](#lectures-and-tutorials)

- [Other Resources](#other-resources)

# Papers

## Survey

**Arxiv**

- Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey [[17 Nov 2022]](https://arxiv.org/abs/2211.10412) [[project]](https://github.com/xuyu0010/awesome-video-domain-adaptation)

- A Survey on Deep Domain Adaptation for LiDAR Perception [[7 Jun 2021]](https://arxiv.org/abs/2106.02377)

- A Comprehensive Survey on Transfer Learning [[7 Nov 2019]](https://arxiv.org/abs/1911.02685)

- Transfer Adaptation Learning: A Decade Survey [[12 Mar 2019]](https://arxiv.org/abs/1903.04687)

- A review of single-source unsupervised domain adaptation [[16 Jan 2019]](https://arxiv.org/abs/1901.05335)

- An introduction to domain adaptation and transfer learning [[31 Dec 2018]](https://arxiv.org/abs/1812.11806v2)

- A Survey of Unsupervised Deep Domain Adaptation [[6 Dec 2018]](https://arxiv.org/abs/1812.02849v2)

- Transfer Learning for Cross-Dataset Recognition: A Survey [[2017]](https://sci-hub.tw/https://arxiv.org/abs/1705.04396)

- Domain Adaptation for Visual Applications: A Comprehensive Survey  [[2017]](https://arxiv.org/abs/1702.05374)

**Journal**

- Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving [[IEEE Access 2023]](https://ieeexplore.ieee.org/document/10128983)

- A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [[TNNLS 2020]](https://arxiv.org/pdf/2009.00155.pdf)

- Deep Visual Domain Adaptation: A Survey [[Neurocomputing 2018]](https://arxiv.org/abs/1802.03601v4)

- A Survey on Deep Transfer Learning [[ICANN2018]](https://arxiv.org/abs/1808.01974v1)

- Visual domain adaptation: A survey of recent advances [[2015]](https://sci-hub.tw/10.1109/msp.2014.2347059)

## Theory

**Arxiv**

- A Theory of Label Propagation for Subpopulation Shift [[22 Feb 2021]](https://arxiv.org/abs/2102.11203)

- A General Upper Bound for Unsupervised Domain Adaptation [[3 Oct 2019]](https://arxiv.org/abs/1910.01409)

- On Deep Domain Adaptation: Some Theoretical Understandings [[arXiv 15 Nov 2018]](https://arxiv.org/abs/1811.06199)

**Conference**

- Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift [[NeurIPS 2020]](https://arxiv.org/abs/2003.04475)

- Bridging Theory and Algorithm for Domain Adaptation [[ICML2019]](http://proceedings.mlr.press/v97/zhang19i/zhang19i.pdf) [[Pytorch]](https://github.com/thuml/MDD)

- On Learning Invariant Representation for Domain Adaptation [[ICML2019]](https://arxiv.org/abs/1901.09453v1) [[code]](https://github.com/KeiraZhao/On-Learning-Invariant-Representations-for-Domain-Adaptation)

- Unsupervised Domain Adaptation Based on Source-guided Discrepancy [[AAAI2019]](https://arxiv.org/abs/1809.03839)

- Learning Bounds for Domain Adaptation [[NIPS2007]](http://papers.nips.cc/paper/3212-learning-bounds-for-domain-adaptation)

- Analysis of Representations for Domain Adaptation [[NIPS2006]](https://papers.nips.cc/paper/2983-analysis-of-representations-for-domain-adaptation)

**Journal**

- On a Regularization of Unsupervised Domain Adaptation in RKHS [[ACHA2021]](https://www.sciencedirect.com/science/article/abs/pii/S1063520321001032?via%3Dihub)

- Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice [[TPAMI2020]](https://arxiv.org/abs/2002.08681) [[PyTroch]](https://github.com/YBZh/MultiClassDA)

- On generalization in moment-based domain adaptation [[AMAI2020]](https://link.springer.com/article/10.1007/s10472-020-09719-x)

- A theory of learning from different domains [[ML2010]](https://link.springer.com/content/pdf/10.1007%2Fs10994-009-5152-4.pdf)

## Explainable

**Conference**

- Visualizing Adapted Knowledge in Domain Transfer [[CVPR2021]](https://arxiv.org/abs/2104.10602) [[Pytorch]](https://github.com/hou-yz/DA_visualization)

## Unsupervised DA

### Adversarial Methods

**Conference**

- SPA: A Graph Spectral Alignment Perspective for Domain Adaptation [[NeurIPS 2023]](https://arxiv.org/abs/2310.17594) [[Pytorch]](https://github.com/CrownX/SPA)

- Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Reusing_the_Task-Specific_Classifier_as_a_Discriminator_Discriminator-Free_Adversarial_Domain_CVPR_2022_paper.pdf) [[Pytorch]](https://github.com/xiaoachen98/DALN)

- A Closer Look at Smoothness in Domain Adversarial Training [[ICML2022]](https://arxiv.org/abs/2206.08213) [[Pytorch]](https://github.com/val-iisc/SDAT)

- ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation [[NeurIPS2021]](https://arxiv.org/abs/2004.01888) [[Pytorch]](https://github.com/microsoft/UDA)

- Adversarial Unsupervised Domain Adaptation With Conditional and Label Shift: Infer, Align and Iterate [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Adversarial_Unsupervised_Domain_Adaptation_With_Conditional_and_Label_Shift_Infer_ICCV_2021_paper.html)

- Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Gao_Gradient_Distribution_Alignment_Certificates_Better_Adversarial_Domain_Adaptation_ICCV_2021_paper.html)

- Re-energizing Domain Discriminator with Sample Relabeling

for Adversarial Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Jin_Re-Energizing_Domain_Discriminator_With_Sample_Relabeling_for_Adversarial_Domain_Adaptation_ICCV_2021_paper.pdf)

- Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Du_Cross-Domain_Gradient_Discrepancy_Minimization_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf) [[Pytorch]](https://github.com/lijin118/CGDM)

- MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation [[CVPR2021]](https://arxiv.org/abs/2103.13575) [[Pytorch]](https://github.com/microsoft/UDA)

- Self-adaptive Re-weighted Adversarial Domain Adaptation [[IJCAI2020]](https://www.ijcai.org/Proceedings/2020/0440.pdf)

- DIRL: Domain-Invariant Reperesentation Learning Approach for Sim-to-Real Transfer [[CoRL2020]](https://arxiv.org/abs/2011.07589) [[Project]](https://www.sites.google.com/view/dirl)

- SSA-DA: Bi-dimensional feature alignment for cross-domain object detection [[ECCV Workshop 2020]](https://arxiv.org/pdf/2011.07205.pdf)

- Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation [[ECCV2020]](https://arxiv.org/abs/2007.09222) [[PyTorch]](https://github.com/JDAI-CV/FADA)

- MCAR: Adaptive object detection with dual multi-label prediction [[ECCV2020]](https://arxiv.org/pdf/2003.12943.pdf)

- Gradually Vanishing Bridge for Adversarial Domain Adaptation [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Cui_Gradually_Vanishing_Bridge_for_Adversarial_Domain_Adaptation_CVPR_2020_paper.pdf) [[Pytorch]](https://github.com/cuishuhao/GVB)

- Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation [[ICML2020]](https://arxiv.org/abs/2006.04996) [[Pytorch]](https://github.com/xiangdal/implicit_alignment)

- Adversarial-Learned Loss for Domain Adaptation [[AAAI2020]](https://arxiv.org/abs/2001.01046v1)

- Structure-Aware Feature Fusion for Unsupervised Domain Adaptation [[AAAI2020]](https://aaai.org/Papers/AAAI/2020GB/AAAI-ChenQ.8923.pdf)

- Adversarial Domain Adaptation with Domain Mixup [[AAAI2020]](https://arxiv.org/abs/1912.01805v1) [[Pytorch]](https://github.com/ChrisAllenMing/Mixup_for_UDA)

- Discriminative Adversarial Domain Adaptation [[AAAI2020]](https://arxiv.org/abs/1911.12036v1) [[Pytorch]](https://github.com/huitangtang/DADA-AAAI2020)

- Bi-Directional Generation for Unsupervised Domain Adaptation [[AAAI2020]](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-YangG.1084.pdf)

- Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning [[ICASSP 2020]](https://arxiv.org/abs/2002.08587)

- Curriculum based Dropout Discriminator for Domain Adaptation [[BMVC2019]](https://arxiv.org/pdf/1907.10628.pdf) [[Project]](https://delta-lab-iitk.github.io/CD3A/)

- Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition [[IJCNN2019]](https://arxiv.org/abs/1903.10601) [[Matlab]](https://github.com/hellowangqian/domain-adaptation-capls)

- Transfer Learning with Dynamic Adversarial Adaptation Network [[ICDM2019]](https://arxiv.org/abs/1909.08184)

- Joint Adversarial Domain Adaptation [[ACM MM2019]](https://dl.acm.org/citation.cfm?id=3351070)

- Cycle-consistent Conditional Adversarial Transfer Networks [[ACM MM2019]](https://dl.acm.org/citation.cfm?id=3350902) [[Pytorch]](https://github.com/lijin118/3CATN)

- Learning Disentangled Semantic Representation for Domain Adaptation [[IJCAI2019]](https://www.ijcai.org/proceedings/2019/0285.pdf) [[Tensorflow]](https://github.com/DMIRLAB-Group/DSR)

- Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation [[ICML2019]](http://proceedings.mlr.press/v97/chen19i/chen19i.pdf) [[Pytorch]](https://github.com/thuml/Batch-Spectral-Penalization)

- Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers [[ICML2019]](http://proceedings.mlr.press/v97/liu19b/liu19b.pdf) [[Pytorch]](https://github.com/thuml/Transferable-Adversarial-Training)

- Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Drop_to_Adapt_Learning_Discriminative_Features_for_Unsupervised_Domain_Adaptation_ICCV_2019_paper.pdf) [[PyTorch]](https://github.com/postBG/DTA.pytorch)

- Cluster Alignment with a Teacher for Unsupervised Domain Adaptation [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Deng_Cluster_Alignment_With_a_Teacher_for_Unsupervised_Domain_Adaptation_ICCV_2019_paper.pdf) [[Tensorflow]](https://github.com/thudzj/CAT)

- Unsupervised Domain Adaptation via Regularized Conditional Alignment [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Cicek_Unsupervised_Domain_Adaptation_via_Regularized_Conditional_Alignment_ICCV_2019_paper.pdf)

- Attending to Discriminative Certainty for Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Kurmi_Attending_to_Discriminative_Certainty_for_Domain_Adaptation_CVPR_2019_paper.pdf) [[Project]](https://delta-lab-iitk.github.io/CADA/)

- GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_GCAN_Graph_Convolutional_Adversarial_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf)

- Domain-Symmetric Networks for Adversarial Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Domain-Symmetric_Networks_for_Adversarial_Domain_Adaptation_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/YBZh/SymNets)

- DLOW: Domain Flow for Adaptation and Generalization [[CVPR2019 Oral]](https://arxiv.org/pdf/1812.05418.pdf)

- Progressive Feature Alignment for Unsupervised Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Progressive_Feature_Alignment_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf) [[Tensorflow]](https://github.com/Xiewp/PFAN)

- Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Tran_Gotta_Adapt_Em_All_Joint_Pixel_and_Feature-Level_Domain_Adaptation_CVPR_2019_paper.pdf) 

- Looking back at Labels: A Class based Domain Adaptation Technique [[IJCNN2019]](https://arxiv.org/abs/1904.01341) [[Project]](https://vinodkkurmi.github.io/DiscriminatorDomainAdaptation/)

- Consensus Adversarial Domain Adaptation [[AAAI2019]](https://aaai.org/ojs/index.php/AAAI/article/view/4552)

- Transferable Attention for Domain Adaptation [[AAAI2019]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/transferable-attention-aaai19.pdf)

- Exploiting Local Feature Patterns for Unsupervised Domain Adaptation [[AAAI2019]](https://arxiv.org/abs/1811.05042v2)

- Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation [[ICLR2019]](https://openreview.net/forum?id=B1G9doA9F7)

- Conditional Adversarial Domain Adaptation [[NIPS2018]](http://papers.nips.cc/paper/7436-conditional-adversarial-domain-adaptation) [[Pytorch(official)]](https://github.com/thuml/CDAN)  [[Pytorch(third party)]](https://github.com/thuml/CDAN)

- Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Baris_Gecer_Semi-supervised_Adversarial_Learning_ECCV_2018_paper.pdf)

- Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Guoliang_Kang_Deep_Adversarial_Attention_ECCV_2018_paper.pdf)

- Learning Semantic Representations for Unsupervised Domain Adaptation [[ICML2018]](http://proceedings.mlr.press/v80/xie18c.html) [[TensorFlow(Official)]](https://github.com/Mid-Push/Moving-Semantic-Transfer-Network)

- CyCADA: Cycle-Consistent Adversarial Domain Adaptation [[ICML2018]](http://proceedings.mlr.press/v80/hoffman18a.html) [[Pytorch(official)]](https://github.com/jhoffman/cycada_release)

- From source to target and back: Symmetric Bi-Directional Adaptive GAN [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Russo_From_Source_to_CVPR_2018_paper.pdf) [[Keras(Official)]](https://github.com/engharat/SBADAGAN) [[Pytorch]](https://github.com/naoto0804/pytorch-SBADA-GAN)

- Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Detach_and_Adapt_CVPR_2018_paper.pdf) [[Tensorflow]](https://github.com/ycliu93/CDRD)

- Maximum Classifier Discrepancy for Unsupervised Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Saito_Maximum_Classifier_Discrepancy_CVPR_2018_paper.pdf) [[Pytorch(Official)]](https://github.com/mil-tokyo/MCD_DA)

- Adversarial Feature Augmentation for Unsupervised Domain Adaptation [[CVPR2018]](https://arxiv.org/abs/1711.08561) [[TensorFlow(Official)]](https://github.com/ricvolpi/adversarial-feature-augmentation)

- Duplex Generative Adversarial Network for Unsupervised Domain Adaptation [[CVPR2018]](http://vipl.ict.ac.cn/uploadfile/upload/2018041610083083.pdf) [[Pytorch(Official)]](http://vipl.ict.ac.cn/view_database.php?id=6)

- Generate To Adapt: Aligning Domains using Generative Adversarial Networks [[CVPR2018]](https://arxiv.org/abs/1704.01705) [[Pytorch(Official)]](https://github.com/yogeshbalaji/Generate_To_Adapt)

- Image to Image Translation for Domain Adaptation [[CVPR2018]](https://arxiv.org/abs/1712.00479)

- Unsupervised Domain Adaptation with Similarity Learning [[CVPR2018]](https://arxiv.org/abs/1711.08995)

- Conditional Generative Adversarial Network for Structured Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hong_Conditional_Generative_Adversarial_CVPR_2018_paper.pdf) 

- Collaborative and Adversarial Network for Unsupervised Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Collaborative_and_Adversarial_CVPR_2018_paper.pdf) [[Pytorch]](https://github.com/zhangweichen2006/iCAN)

- Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Re-Weighted_Adversarial_Adaptation_CVPR_2018_paper.pdf)

- Multi-Adversarial Domain Adaptation [[AAAI2018]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/multi-adversarial-domain-adaptation-aaai18.pdf) [[Caffe(Official)]](https://github.com/thuml/MADA)

- Wasserstein Distance Guided Representation Learning for Domain Adaptation [[AAAI2018]](https://arxiv.org/abs/1707.01217) [[TensorFlow(official)]](https://github.com/RockySJ/WDGRL) [[Pytorch]](https://github.com/jvanvugt/pytorch-domain-adaptation)

- Incremental Adversarial Domain Adaptation for Continually Changing Environments [[ICRA2018]](https://arxiv.org/abs/1712.07436)

- Adversarial Dropout Regularization [[ICLR2018]](https://openreview.net/forum?id=HJIoJWZCZ)

- A DIRT-T Approach to Unsupervised Domain Adaptation [[ICLR2018 Poster]](https://openreview.net/forum?id=H1q-TM-AW) [[Tensorflow(Official)]](https://github.com/RuiShu/dirt-t)

- Label Efficient Learning of Transferable Representations acrosss Domains and Tasks [[NIPS2017]](http://vision.stanford.edu/pdf/luo2017nips.pdf) [[Project]](http://alan.vision/nips17_website/)

- Adversarial Discriminative Domain Adaptation [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Tzeng_Adversarial_Discriminative_Domain_CVPR_2017_paper.pdf)  [[Tensorflow(Official)]](https://github.com/erictzeng/adda) [[Pytorch]](https://github.com/corenel/pytorch-adda)

- Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bousmalis_Unsupervised_Pixel-Level_Domain_CVPR_2017_paper.pdf) [[Tensorflow(Official)]](https://github.com/tensorflow/models/tree/master/research/domain_adaptation) [[Pytorch]](https://github.com/vaibhavnaagar/pixelDA_GAN)

- Domain Separation Networks [[NIPS2016]](http://papers.nips.cc/paper/6254-domain-separation-networks)

- Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation [[ECCV2016]](https://arxiv.org/abs/1607.03516)

- Domain-Adversarial Training of Neural Networks [[JMLR2016]](http://www.jmlr.org/papers/volume17/15-239/15-239.pdf)

- Unsupervised Domain Adaptation by Backpropagation [[ICML2015]](http://proceedings.mlr.press/v37/ganin15.pdf) [[Caffe(Official)]](https://github.com/ddtm/caffe/tree/grl) [[Tensorflow]](https://github.com/shucunt/domain_adaptation) [[Pytorch]](https://github.com/fungtion/DANN)

**Journal**

- Incremental Unsupervised Domain-Adversarial Training of Neural Networks [[TNNLS 2020]](https://ieeexplore.ieee.org/document/9216604)

- Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice [[TPAMI2020]](https://arxiv.org/abs/2002.08681) [[PyTroch]](https://github.com/YBZh/MultiClassDA)

- Adversarial Learning and Interpolation Consistency for Unsupervised Domain Adaptation [[IEEE ACCESS]](https://ieeexplore.ieee.org/document/8913529)

- TarGAN: Generating target data with class labels for unsupervised domain adaptation [[Knowledge-Based Systems]]()

**Arxiv**

- Bi-Directional Generation for Unsupervised Domain Adaptation [[12 Feb 2020]](https://arxiv.org/abs/2002.04869v1)

- Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation [[19 Feb 2020]](https://arxiv.org/abs/2002.08041v1) [[Tensorflow]](https://github.com/haitran14/gada)

- Learning Domain Adaptive Features with Unlabeled Domain Bridges [[10 Dec 2019]](https://arxiv.org/abs/1912.05004v1)

- Reducing Domain Gap via Style-Agnostic Networks [[25 Oct 2019]](https://arxiv.org/abs/1910.11645)

- Generalized Domain Adaptation with Covariate and

Label Shift CO-ALignment [[23 Oct 2019]](https://arxiv.org/abs/1910.10320)

- Adversarial Variational Domain Adaptation [[25 Sep 2019]](https://arxiv.org/abs/1909.11651)

- Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation [[arXiv 13 Sep 2019]](https://arxiv.org/abs/1909.05288)

- SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation [[arXiv 11 Jun 2019]](https://arxiv.org/abs/1906.04338v1)

- Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation [[arXiv 10 Jun 2019]](https://arxiv.org/abs/1906.04053v1)

- Adversarial Domain Adaptation Being Aware of Class Relationships [[arXiv 28 May 2019]](https://arxiv.org/abs/1905.11931v1)

- Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption [[arXiv 30 Nov 2018]](https://arxiv.org/abs/1811.12751)

- Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks [[arXiv 17 Feb 2019]](https://arxiv.org/abs/1902.06328v1)

- DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification [[arXiv 30 Dec 2018]](https://arxiv.org/abs/1812.11478)

- Unsupervised Domain Adaptation using Generative Models and Self-ensembling [[arXiv 2 Dec 2018]](https://arxiv.org/abs/1812.00479)

- Domain Confusion with Self Ensembling for Unsupervised Adaptation [[arXiv 10 Oct 2018]](https://arxiv.org/abs/1810.04472)

- Improving Adversarial Discriminative Domain Adaptation [[arXiv 10 Sep 2018]](https://arxiv.org/abs/1809.03625)

- M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [[arXiv 6 Jul 2018]](https://arxiv.org/abs/1807.02552v1) [[Pytorch(official)]](https://github.com/IssamLaradji/M-ADDA)

- Factorized Adversarial Networks for Unsupervised Domain Adaptation [[arXiv 4 Jun 2018]](https://arxiv.org/abs/1806.01376v1)

- DiDA: Disentangled Synthesis for Domain Adaptation [[arXiv 21 May 2018]](https://arxiv.org/abs/1805.08019v1)

- Unsupervised Domain Adaptation with Adversarial Residual Transform Networks [[arXiv 25 Apr 2018]](https://arxiv.org/abs/1804.09578)

- Causal Generative Domain Adaptation Networks [[arXiv 28 Jun 2018]](https://arxiv.org/abs/1804.04333v3)

### Distance-based Methods

**Journal**

- Transferable Representation Learning with Deep Adaptation Networks [[TPAMI]](https://ieeexplore.ieee.org/document/8454781)

- Robust unsupervised domain adaptation for neural networks via moment alignment [[InfSc2019]](https://www.sciencedirect.com/science/article/abs/pii/S0020025519300301)

**Conference**

- Domain Conditioned Adaptation Network [[AAAI2020]](https://arxiv.org/abs/2005.06717) [[Pytorch]](https://github.com/BIT-DA/DCAN)

- HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation [[AAAI2020]](https://arxiv.org/abs/1912.11976) [[Tensorflow]](https://github.com/chenchao666/HoMM-Master)

- Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Balaji_Normalized_Wasserstein_for_Mixture_Distributions_With_Applications_in_Adversarial_Learning_ICCV_2019_paper.pdf)

- Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation [[AAAI2019]](https://arxiv.org/abs/1808.09347v2)

- Residual Parameter Transfer for Deep Domain Adaptation [[CVPR2018]](https://arxiv.org/abs/1711.07714)

- Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation [[AAAI2018]](http://media.cs.tsinghua.edu.cn/~multimedia/cuipeng/papers/DATN.pdf)

- Central Moment Discrepancy for Unsupervised Domain Adaptation [[ICLR2017]](https://openreview.net/pdf?id=SkB-_mcel), [[InfSc2019]](https://arxiv.org/pdf/1711.06114.pdf), [[code]](https://github.com/wzell/cmd)

- Deep CORAL: Correlation Alignment for Deep Domain Adaptation [[ECCV2016]](https://arxiv.org/abs/1607.01719)

- Learning Transferable Features with Deep Adaptation Networks [[ICML2015]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-adaptation-networks-icml15.pdf)[[code]](https://github.com/thuml/DAN)

- Unsupervised Domain Adaptation with Residual Transfer Networks [[NIPS2016]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/residual-transfer-network-nips16.pdf) [[code]](https://github.com/thuml/Xlearn)

- Deep Transfer Learning with Joint Adaptation Networks [[ICML2017]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/joint-adaptation-networks-icml17.pdf) [[code]](https://github.com/thuml/Xlearn)

**Arxiv**

- Deep Domain Confusion: Maximizing for Domain Invariance [[Arxiv 2014]](https://arxiv.org/abs/1412.3474)

### Information-based Methods

- Hypothesis Disparity Regularized Mutual Information Maximization [[AAAI2021]](https://arxiv.org/abs/2012.08072)

### Optimal Transport

**Conference**

- Global-Local Regularization Via Distributional Robustness [[AISTATS2023]](https://arxiv.org/abs/2203.00553) [[Pytorch]](https://github.com/VietHoang1512/GLOT/)

- MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning [[UAI2021]](https://auai.org/uai2021/pdf/uai2021.106.pdf)

- LAMDA: Label Matching Deep Domain Adaptation [[ICML2021]](http://proceedings.mlr.press/v139/le21a.html)

- TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport [[IJCAI2021]](https://www.ijcai.org/proceedings/2021/0394.pdf) 

- Unbalanced minibatch Optimal Transport; applications to Domain Adaptation [[ICML2021]](https://arxiv.org/abs/2103.03606) [[Pytorch]](https://github.com/kilianFatras/JUMBOT)

- Graph Optimal Transport for Cross-Domain Alignment [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/971-Paper.pdf)

- Margin-aware Adversarial Domain Adaptation with Optimal Transport [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/2666-Paper.pdf) [[code]](https://github.com/sofiendhouib/MADAOT)

- Metric Learning in Optimal Transport for Domain Adaptation [[IJCAI2020]](https://www.ijcai.org/Proceedings/2020/0299.pdf)

- Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Reliable_Weighted_Optimal_Transport_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.pdf)

- Enhanced Transport Distance for Unsupervised Domain Adaptation [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Enhanced_Transport_Distance_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.pdf) [[Pytorch]](https://github.com/yimzhai3/ETD)

- Differentially Private Optimal Transport: Application to Domain Adaptation [[IJCAI2019]](https://www.ijcai.org/proceedings/2019/0395.pdf)

- DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Bharath_Bhushan_Damodaran_DeepJDOT_Deep_Joint_ECCV_2018_paper.pdf) [[Keras]](https://github.com/bbdamodaran/deepJDOT)

- Joint Distribution Optimal Transportation for Domain Adaptation [[NIPS2017]](http://papers.nips.cc/paper/6963-joint-distribution-optimal-transportation-for-domain-adaptation.pdf) [[python]](https://github.com/rflamary/JDOT) [[Python Optimal Transport Library]](https://github.com/rflamary/POT)

**Arxiv**

- CDOT: Continuous Domain Adaptation using Optimal Transport [[20 Sep 2019]](https://arxiv.org/abs/1909.11448)

### Incremental Methods

- Incremental Unsupervised Domain-Adversarial Training of Neural Networks [[TNNLS 2020]](https://ieeexplore.ieee.org/document/9216604)

### Semi-Supervised-Learning-Based Methods

- Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490749.pdf)

- Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners [[arXiv 2021]]((https://arxiv.org/pdf/2106.00417.pdf))[[Pytorch]](https://github.com/YBZh/Bridging_UDA_SSL)

### Self-training-Based Methods

- Cycle Self-Training for Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/c1fea270c48e8079d8ddf7d06d26ab52-Abstract.html)

- Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark [[ICCV Workshop 2021]](https://arxiv.org/abs/2108.10840) [[Pytorch]](https://github.com/bupt-ai-cz/Meta-SelfLearning)

- Instance Adaptive Self-Training for Unsupervised Domain Adaptation [[ECCV 2020]](https://arxiv.org/abs/2008.12197) [[Pytorch]](https://github.com/bupt-ai-cz/IAST-ECCV2020)

- Self-training Avoids Using Spurious Features Under Domain Shift [[NeurIPS 2020]](https://arxiv.org/abs/2006.10032)

- Two-phase Pseudo Label Densification for Self-training based Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580528.pdf)

**Arxiv**

- Probabilistic Contrastive Learning for Domain Adaptation [[arXiv 20211]](https://arxiv.org/abs/2111.06021) [[Pytorch]](https://github.com/ljjcoder/Probabilistic-Contrastive-Learning)

- Gradual Domain Adaptation via Self-Training of Auxiliary Models[[arXiv 2021]](https://arxiv.org/pdf/2106.09890.pdf)[[Pytorch]](https://github.com/YBZh/AuxSelfTrain)

### Self-Supervised Methods

**Conference**

- Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650494.pdf)

**Arxiv**

- Unsupervised Domain Adaptation through Self-Supervision [[arXiv 26 Sep 2019]](https://arxiv.org/abs/1909.11825)

### Transformer-based Methods

**Conference**

- Safe Self-Refinement for Transformer-Based Domain Adaptation [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.pdf) [[Pytorch](https://github.com/tsun/SSRT)

### Other Methods

**Conference**

- Prior Knowledge Guided Unsupervised Domain Adaptation [[ECCV2022]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136930628.pdf) [[Pytorch]](https://github.com/tsun/KUDA)

- Revisiting Unsupervised Domain Adaptation Models: a Smoothness Perspective [[ACCV2022]](https://openaccess.thecvf.com/content/ACCV2022/html/Wang_Revisiting_Unsupervised_Domain_Adaptation_Models_a_Smoothness_Perspective_ACCV_2022_paper.html) [[Pytorch]](https://github.com/Wang-Xiaodong1899/LeCo_UDA)

- Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/4f284803bd0966cc24fa8683a34afc6e-Abstract.html)

- Pareto Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/6ba3af5d7b2790e73f0de32e5c8c1798-Abstract.html)

- ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/731c83db8d2ff01bdc000083fd3c3740-Abstract.html)

- A Prototype-Oriented Framework for Unsupervised Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/8edd72158ccd2a879f79cb2538568fdc-Abstract.html)

- Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/90cc440b1b8caa520c562ac4e4bbcb51-Abstract.html)

- SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Prabhu_SENTRY_Selective_Entropy_Optimization_via_Committee_Consistency_for_Unsupervised_Domain_ICCV_2021_paper.html)

- Transporting Causal Mechanisms for Unsupervised Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Yue_Transporting_Causal_Mechanisms_for_Unsupervised_Domain_Adaptation_ICCV_2021_paper.html)

- Semantic Concentration for Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Li_Semantic_Concentration_for_Domain_Adaptation_ICCV_2021_paper.html) 

- FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf)

- Domain Adaptation With Auxiliary Target Domain-Oriented Classifier [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper.pdf)

- Conditional Bures Metric for Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.pdf)

- DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_DRANet_Disentangling_Representation_and_Adaptation_Networks_for_Unsupervised_Cross-Domain_Adaptation_CVPR_2021_paper.pdf)

- Visualizing Adapted Knowledge in Domain Transfer [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Visualizing_Adapted_Knowledge_in_Domain_Transfer_CVPR_2021_paper.pdf) [[Pytorch]](https://github.com/hou-yz/DA_visualization)

- Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Sharma_Instance_Level_Affinity-Based_Transfer_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf) [[code coming soon]](https://github.com/astuti/ILA-DA)

- Dynamic Domain Adaptation for Efficient Inference [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Domain_Adaptation_for_Efficient_Inference_CVPR_2021_paper.pdf) [[Pytorch]](https://github.com/BIT-DA/DDA)

- Transferable Semantic Augmentation for Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Transferable_Semantic_Augmentation_for_Domain_Adaptation_CVPR_2021_paper.pdf) [[Pytorch]](https://github.com/BIT-DA/TSA)

- MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_MetaAlign_Coordinating_Domain_Alignment_and_Classification_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf)

- DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation [[CVPR2021]](https://arxiv.org/abs/2103.13447v1)

- Dynamic Weighted Learning for Unsupervised Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_Dynamic_Weighted_Learning_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf)

- Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift [[NeurIPS 2020]](https://arxiv.org/abs/2003.04475)

- Transferable Calibration with Lower Bias and Variance in Domain Adaptation [[NeurIPS 2020]](https://arxiv.org/abs/2007.08259)

- A Dictionary Approach to Domain-Invariant Learning in Deep Networks [[NeurIPS 2020]](https://arxiv.org/abs/1909.11285)

- Heuristic Domain Adaptation [[NeurIPS2020]](https://arxiv.org/abs/2011.14540) [[Pytorch]](https://github.com/cuishuhao/HDA)

- Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search [[ECCV2020]](https://arxiv.org/abs/2006.08696)[[code]](https://github.com/ambekarsameer96/GLSS)

- Mind the Discriminability: Asymmetric Adversarial Domain Adaptation [[ECCV2020]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690579.pdf)

- Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510749.pdf)

- CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530732.pdf)

- Minimum Class Confusion for Versatile Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660460.pdf)

- Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift [[ECCV2020]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2472_ECCV_2020_paper.php) [[Pytorch]](https://github.com/iiyama-lab/PS-VAEs)

- Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation [[ECCV2020]](https://arxiv.org/pdf/2007.07695.pdf) [[PyTorch]](https://github.com/YBZh/Label-Propagation-with-Augmented-Anchors)

- Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering [[CVPR2020 Oral]](http://arxiv.org/abs/2003.08607) [[Pytorch]](https://github.com/huitangtang/SRDC-CVPR2020)

- Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations [[CVPR2020 Oral]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Cui_Towards_Discriminability_and_Diversity_Batch_Nuclear-Norm_Maximization_Under_Label_Insufficient_CVPR_2020_paper.pdf) [[Pytorch]](https://github.com/cuishuhao/BNM)

- Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.pdf)

- Spherical Space Domain Adaptation With Robust Pseudo-Label Loss [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Gu_Spherical_Space_Domain_Adaptation_With_Robust_Pseudo-Label_Loss_CVPR_2020_paper.pdf) [[Pytorch]](https://github.com/XJTU-XGU/RSDA)

- Stochastic Classifiers for Unsupervised Domain Adaptation [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lu_Stochastic_Classifiers_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.pdf)

- Structure Preserving Generative Cross-Domain Learning [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Xia_Structure_Preserving_Generative_Cross-Domain_Learning_CVPR_2020_paper.pdf)

- Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Ye_Light-weight_Calibrator_A_Separable_Component_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.pdf) [[code]](https://github.com/yeshaokai/Calibrator-Domain-Adaptation)

- Domain Adaptive Multiflow Networks [[ICLR2020]](https://openreview.net/forum?id=rJxycxHKDS)

- Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment [[AAAI2020]](https://arxiv.org/abs/2002.08675v1)

- Visual Domain Adaptation by Consensus-based Transfer to Intermediate Domain [[Paper]](https://aaai.org/Papers/AAAI/2020GB/AAAI-ChoiJ.3612.pdf)

- Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling [[AAAI2020]](https://arxiv.org/abs/1911.07982) [[Matlab]](https://github.com/hellowangqian/domain-adaptation-capls)

- CUDA: Contradistinguisher for Unsupervised Domain Adaptation [[ICDM2019]](https://arxiv.org/abs/1909.03442)

- Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment [[ICML2019]](http://proceedings.mlr.press/v97/wu19f/wu19f.pdf)

- Batch Weight for Domain Adaptation With Mass Shift [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Binkowski_Batch_Weight_for_Domain_Adaptation_With_Mass_Shift_ICCV_2019_paper.pdf)

- Switchable Whitening for Deep Representation Learning [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Pan_Switchable_Whitening_for_Deep_Representation_Learning_ICCV_2019_paper.pdf) [[pytorch]](https://github.com/XingangPan/Switchable-Whitening)

- Confidence Regularized Self-Training [[ICCV2019 Oral]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zou_Confidence_Regularized_Self-Training_ICCV_2019_paper.pdf) [[Pytorch]](https://github.com/yzou2/CRST)

- Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Larger_Norm_More_Transferable_An_Adaptive_Feature_Norm_Approach_for_ICCV_2019_paper.pdf) [[Pytorch(official)]](https://github.com/jihanyang/AFN)

- Transferrable Prototypical Networks for Unsupervised Domain Adaptation [[CVPR2019(Oral)]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Pan_Transferrable_Prototypical_Networks_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf)

- Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_Sliced_Wasserstein_Discrepancy_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf)

- Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss [[CVPR 2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Roy_Unsupervised_Domain_Adaptation_Using_Feature-Whitening_and_Consensus_Loss_CVPR_2019_paper.pdf)  [[Pytorch]](https://github.com/roysubhankar/dwt-domain-adaptation)

- Domain Specific Batch Normalization for Unsupervised Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Domain-Specific_Batch_Normalization_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/wgchang/DSBN)

- AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Mancini_AdaGraph_Unifying_Predictive_and_Continuous_Domain_Adaptation_Through_Graphs_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/mancinimassimiliano/adagraph)

- Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Unsupervised_Visual_Domain_Adaptation_A_Deep_Max-Margin_Gaussian_Process_Approach_CVPR_2019_paper.pdf) [[Project]](https://seqam-lab.github.io/GPDA/)

- Contrastive Adaptation Network for Unsupervised Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Kang_Contrastive_Adaptation_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/kgl-prml/Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation)

- Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liang_Distant_Supervised_Centroid_Shift_A_Simple_and_Efficient_Approach_to_CVPR_2019_paper.pdf)

- Unsupervised Domain Adaptation via Calibrating Uncertainties [[CVPRW2019]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Uncertainty%20and%20Robustness%20in%20Deep%20Visual%20Learning/Han_Unsupervised_Domain_Adaptation_via_Calibrating_Uncertainties_CVPRW_2019_paper.pdf)

- Bayesian Uncertainty Matching for Unsupervised Domain Adaptation [[IJCAI2019]](https://arxiv.org/abs/1906.09693v1)

- Unsupervised Domain Adaptation for Distance Metric Learning [[ICLR2019]](https://openreview.net/forum?id=BklhAj09K7)

- Co-regularized Alignment for Unsupervised Domain Adaptation [[NIPS2018]](http://papers.nips.cc/paper/8146-co-regularized-alignment-for-unsupervised-domain-adaptation)

- Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [[TIP 2018]](https://ieeexplore.ieee.org/document/8362753/)

- Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhengming_Ding_Graph_Adaptive_Knowledge_ECCV_2018_paper.pdf)

- Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Aligning_Infinite-Dimensional_Covariance_CVPR_2018_paper.pdf)

- Unsupervised Domain Adaptation with Distribution Matching Machines [[AAAI2018]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/distribution-matching-machines-aaai18.pdf)

- Learning to cluster in order to transfer across domains and tasks [[ICLR2018]](https://openreview.net/forum?id=ByRWCqvT-) [[Bolg]](https://mlatgt.blog/2018/04/29/learning-to-cluster/) [[Pytorch]](https://github.com/GT-RIPL/L2C)

- Self-Ensembling for Visual Domain Adaptation [[ICLR2018]](https://openreview.net/forum?id=rkpoTaxA-)

- Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation [[ICLR2018]](https://openreview.net/forum?id=rJWechg0Z) [[TensorFlow]](https://github.com/pmorerio/minimal-entropy-correlation-alignment)

- Associative Domain Adaptation [[ICCV2017]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Haeusser_Associative_Domain_Adaptation_ICCV_2017_paper.pdf) [[TensorFlow]](https://github.com/haeusser/learning_by_association) [[Pytorch]](https://github.com/corenel/pytorch-atda)

- AutoDIAL: Automatic DomaIn Alignment Layers [[ICCV2017]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Carlucci_AutoDIAL_Automatic_DomaIn_ICCV_2017_paper.pdf)

- Asymmetric Tri-training for Unsupervised Domain Adaptation [[ICML2017]](http://proceedings.mlr.press/v70/saito17a.html) [[TensorFlow]](https://github.com/ksaito-ut/atda)

- Learning Transferrable Representations for Unsupervised Domain Adaptation [[NIPS2016]](http://papers.nips.cc/paper/6360-learning-transferrable-representations-for-unsupervised-domain-adaptation)

**Journal**

- Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search [[IEEE TMI 2020]](https://ieeexplore.ieee.org/document/9139471)[[code]](https://github.com/prinshul/WBC-Classification-UDA)

- Adaptive Batch Normalization for practical domain adaptation [[Pattern Recognition(2018)]](https://www.sciencedirect.com/science/article/pii/S003132031830092X)

- Unsupervised Domain Adaptation by Mapped Correlation Alignment [[IEEE ACCESS]](https://ieeexplore.ieee.org/abstract/document/8434290/)

**Arxiv**

- Low-confidence Samples Matter for Domain Adaptation [[6 Feb 2022]](https://arxiv.org/abs/2202.02802) [[Pytorch]](https://github.com/zhyx12/MixLRCo)

- Improving Unsupervised Domain Adaptation with Variational Information Bottleneck [[21 Nov 2019]](https://arxiv.org/abs/1911.09310v1)

- Deep causal representation learning for unsupervised domain adaptation [[28 Oct 2019]](https://arxiv.org/abs/1910.12417)

- Domain-invariant Learning using Adaptive Filter

Decomposition [[25 Sep 2019]](https://arxiv.org/abs/1909.11285)

- Discriminative Clustering for Robust Unsupervised Domain Adaptation [[arXiv 30 May 2019]](https://arxiv.org/abs/1905.13331)

- Virtual Mixup Training for Unsupervised Domain Adaptation [[arXiv on 24 May 2019]](https://arxiv.org/abs/1905.04215) [[Tensorflow]](https://github.com/xudonmao/VMT)

- Learning Smooth Representation for Unsupervised Domain Adaptation [[arXiv 26 May 2019]](https://arxiv.org/abs/1905.10748v1)

- Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation [[arXiv 13 Apr 2019]](https://arxiv.org/abs/1904.06490v1)

- Easy Transfer Learning By Exploiting Intra-domain Structures [[arXiv 2 Apr 2019]](https://arxiv.org/abs/1904.01376v1) 

- Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation [[arXiv 30 Jan 2019]](https://arxiv.org/abs/1901.10654v1)

- Domain Alignment with Triplets [[arXiv 22 Jan 2019]](https://arxiv.org/abs/1812.00893v2)

- Deep Discriminative Learning for Unsupervised Domain Adaptation [[arXiv 17 Nov 2018]](https://arxiv.org/abs/1811.07134v1)

## Foundation-Models based DA

**Conference**

-  POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models [[ICML2023]](https://arxiv.org/abs/2305.00350) [[Pytorch]](https://github.com/korawat-tanwisuth/POUF)

## Semi-supervised DA

**Conference**

- Semi-Supervised Domain Adaptation With Source Label Adaptation [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Semi-Supervised_Domain_Adaptation_With_Source_Label_Adaptation_CVPR_2023_paper.html)

- Multi-level Consistency Learning for Semi-supervised Domain Adaptation [[IJCAI 2022]](https://arxiv.org/abs/2205.04066)

- AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation [[ICLR 2022]](https://openreview.net/pdf?id=Q5uh1Nvv5dm)

- CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation [[NeurIPS]](https://proceedings.neurips.cc/paper/2021/hash/288cd2567953f06e460a33951f55daaf-Abstract.html)

- Deep Co-Training With Task Decomposition for Semi-Supervised Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Yang_Deep_Co-Training_With_Task_Decomposition_for_Semi-Supervised_Domain_Adaptation_ICCV_2021_paper.html)

- ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Li_ECACL_A_Holistic_Framework_for_Semi-Supervised_Domain_Adaptation_ICCV_2021_paper.html)

- Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Cross-Domain_Adaptive_Clustering_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.pdf)

- Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Semi-Supervised_Domain_Adaptation_Based_on_Dual-Level_Domain_Mixing_for_Semantic_CVPR_2021_paper.pdf)

- Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [[CVPR2021]](https://arxiv.org/abs/2010.04647)

- Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics [[CVPRW2021]](https://openaccess.thecvf.com/content/CVPR2021W/LLID/papers/Singh_Improving_Semi-Supervised_Domain_Adaptation_Using_Effective_Target_Selection_and_Semantics_CVPRW_2021_paper.pdf) [[Code]](https://github.com/Anurag14/STar-framework)

- Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation [[ECCV2020]](https://arxiv.org/abs/2007.09375v1)

- Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation [[ECCV2020]](https://arxiv.org/abs/2004.04398)

- Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation [[IJCAI2020]](https://www.ijcai.org/Proceedings/2020/130)

- Semi-supervised Domain Adaptation via Minimax Entropy [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Saito_Semi-Supervised_Domain_Adaptation_via_Minimax_Entropy_ICCV_2019_paper.pdf) [[Pytorch]](https://github.com/VisionLearningGroup/SSDA_MME)

**Journal**

- Context-guided entropy minimization for semi-supervised domain adaptation [[Neural Networks]](https://doi.org/10.1016/j.neunet.2022.07.011)  [[pytorch]](https://github.com/NingMa-AI/DEEM)

**Arxiv**

- Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation [[22 Nov 2022]](https://arxiv.org/abs/2211.11975)

- MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation [[ 24 Jul 2020]](https://arxiv.org/abs/2007.12684)

- Opposite Structure Learning for Semi-supervised Domain Adaptation [[6 Feb 2020]](https://arxiv.org/abs/2002.02545v1)

- Reducing Domain Gap via Style-Agnostic Networks [[25 Oct 2019]](https://arxiv.org/abs/1910.11645)

## Weakly-Supervised DA

**Conference**

- Towards Accurate and Robust Domain Adaptation under Noisy Environments [[IJCAI2020]](https://www.ijcai.org/Proceedings/2020/0314.pdf)

- Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Tan_Weakly_Supervised_Open-Set_Domain_Adaptation_by_Dual-Domain_Collaboration_CVPR_2019_paper.pdf)

- Transferable Curriculum for Weakly-Supervised Domain Adaptation [[AAAI2019]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/transferable-curriculum-aaai19.pdf)

**Arxiv**

- Butterfly: Robust One-step Approach towards Wildly-unsupervised Domain Adaptation [[arXiv on 19 May 2019]](https://arxiv.org/abs/1905.07720v1)

## Zero-shot DA

**Conference**

- Collaborative Learning With Disentangled Features for Zero-Shot Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Jhoo_Collaborative_Learning_With_Disentangled_Features_for_Zero-Shot_Domain_Adaptation_ICCV_2021_paper.pdf)

- Zero-Shot Day-Night Domain Adaptation with a Physics Prior [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Lengyel_Zero-Shot_Day-Night_Domain_Adaptation_With_a_Physics_Prior_ICCV_2021_paper.pdf)

- High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730222.pdf)

- Adversarial Learning for Zero-shot Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660324.pdf)

- HGNet: Hybrid Generative Network for Zero-shot Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720052.pdf)

- Zero-shot Domain Adaptation Based on Attribute Information [[ACML2019]](http://proceedings.mlr.press/v101/ishii19a.html)

- Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Conditional_Coupled_Generative_Adversarial_Networks_for_Zero-Shot_Domain_Adaptation_ICCV_2019_paper.pdf)

- Generalized Zero-Shot Learning with Deep Calibration Network [[NIPS2018]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-calibration-network-nips18.pdf)

- Zero-Shot Deep Domain Adaptation [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Kuan-Chuan_Peng_Zero-Shot_Deep_Domain_ECCV_2018_paper.pdf)

## One-shot DA

**Conference**

- Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation [[NeurIPS2020]](https://proceedings.neurips.cc/paper/2020/hash/ed265bc903a5a097f61d3ec064d96d2e-Abstract.html) [[Pytorch]](https://github.com/RoyalVane/ASM)

- One-Shot Adaptation of Supervised Deep Convolutional Models [[ICLR Workshop 2014]](https://arxiv.org/abs/1312.6204)

**Arxiv**

- One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning [[arxiv]](https://arxiv.org/abs/1802.01557)

## Few-shot UDA

**Conference**

- Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation

[[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Prototypical_Cross-Domain_Self-Supervised_Learning_for_Few-Shot_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html) [[Pytorch]](https://github.com/zhengzangw/PCS-FUDA) [[Project]](http://xyue.io/pcs-fuda/)

**Arxiv**

- Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels [[arXiv 18 Mar 2020]](https://arxiv.org/pdf/2003.08264.pdf)

## Few-shot DA

**Conference**

- Domain-Adaptive Few-Shot Learning[[WACV2021]](https://openaccess.thecvf.com/content/WACV2021/papers/Zhao_Domain-Adaptive_Few-Shot_Learning_WACV_2021_paper.pdf) [[Pytorch]](https://github.com/dingmyu/DAPN)

- Few-shot Domain Adaptation by Causal Mechanism Transfer [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/1121-Paper.pdf) [[Pytorch]](https://github.com/takeshi-teshima/few-shot-domain-adaptation-by-causal-mechanism-transfer)

- Few-Shot Adaptive Faster R-CNN [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Few-Shot_Adaptive_Faster_R-CNN_CVPR_2019_paper.html)

- d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding [[CVPR2019 Oral]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_d-SNE_Domain_Adaptation_Using_Stochastic_Neighborhood_Embedding_CVPR_2019_paper.pdf)

- Few-Shot Adversarial Domain Adaptation [[NIPS2017]](http://papers.nips.cc/paper/7244-few-shot-adversarial-domain-adaptation)

**Arxiv**

- Feature transformation ensemble model with batch spectral regularization for cross-domain few-shot classification [[arXiv 18 May 2020]](https://arxiv.org/pdf/2005.08463.pdf) [[Pytorch]](https://github.com/liubingyuu/FTEM_BSR_CDFSL)

- Ensemble model with batch spectral regularization and data blending for cross-domain few-shot learning with unlabeled data [[arXiv 8 June 2020]](https://arxiv.org/pdf/2006.04323.pdf) [[Pytorch]](https://github.com/123zhen123/BSDB-CDFSL_Track)

## Partial DA

**Conference**

- Implicit Semantic Response Alignment for Partial Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/731b03008e834f92a03085ef47061c4a-Abstract.html) [[Pytorch]](https://github.com/implicit-seman-align/Implicit-Semantic-Response-Alignment)

- Adversarial Reweighting for Partial Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/7ce3284b743aefde80ffd9aec500e085-Abstract.html)

- A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560120.pdf) [[Pytorch]](https://github.com/tim-learn/BA3US)

- Discriminative Partial Domain Adversarial Network [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720630.pdf)

- Selective Transfer With Reinforced Transfer Network for Partial Domain Adaptation [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Selective_Transfer_With_Reinforced_Transfer_Network_for_Partial_Domain_Adaptation_CVPR_2020_paper.pdf)

- Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation [[ACM MM2020]](https://dl.acm.org/doi/abs/10.1145/3394171.3413986)

- Multi-Weight Partial Domain Adaptation [[BMVC2019]](https://bmvc2019.org/wp-content/uploads/papers/0406-paper.pdf)

- Learning to Transfer Examples for Partial Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Cao_Learning_to_Transfer_Examples_for_Partial_Domain_Adaptation_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/thuml/ETN)

- Partial Adversarial Domain Adaptation [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper.pdf) [[Pytorch(Official)]](https://github.com/thuml/PADA)

- Importance Weighted Adversarial Nets for Partial Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Importance_Weighted_Adversarial_CVPR_2018_paper.html) [[Caffe]](https://github.com/hellojing89/weightedGANpartialDA)

- Partial Transfer Learning with Selective Adversarial Networks [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_Partial_Transfer_Learning_CVPR_2018_paper.pdf)[[paper weekly]](http://www.paperweekly.site/papers/1388) [[Pytorch(Official) & Caffe(official)]](https://github.com/thuml/SAN)

**Journal**

- Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice [[TPAMI2020]](https://arxiv.org/abs/2002.08681) [[PyTroch]](https://github.com/YBZh/MultiClassDA)

**Arxiv**

- Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation [[arXiv 06 Dec 2020]](https://arxiv.org/abs/2012.03358)

- Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice [[20 Feb 2020]](https://arxiv.org/pdf/2002.08681.pdf) [[PyTroch]](https://github.com/YBZh/MultiClassDA)

- Tackling Partial Domain Adaptation with Self-Supervision [[arXiv 12 Jun 2019]](https://arxiv.org/abs/1906.05199v1)

- Domain Adversarial Reinforcement Learning for Partial Domain Adaptation [[arXiv 10 May 2019]](https://arxiv.org/abs/1905.04094v1)

## Open Set DA

**Conference**

- Towards Novel Target Discovery Through Open-Set Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Jing_Towards_Novel_Target_Discovery_Through_Open-Set_Domain_Adaptation_ICCV_2021_paper.html)

- On the Effectiveness of Image Rotation for Open Set Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610409.pdf) [[Pytorch]](https://github.com/silvia1993/ROS)

- Multi-Source Open-Set Deep Adversarial Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710732.pdf)

- Progressive Graph Learning for Open-Set Domain Adaptation [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/136-Paper.pdf) [[Pytorch]](https://github.com/BUserName/PGL)

- Joint Partial Optimal Transport for Open Set Domain Adaptation [[IJCAI2020]](https://www.ijcai.org/Proceedings/2020/0352.pdf)

- Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Exploring_Category-Agnostic_Clusters_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.pdf)

- Towards Inheritable Models for Open-Set Domain Adaptation [[CVPR 2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Kundu_Towards_Inheritable_Models_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.pdf) [[Project]](https://sites.google.com/view/inheritune)

- Attract or Distract: Exploit the Margin of Open Set [[ICCV2019]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Feng_Attract_or_Distract_Exploit_the_Margin_of_Open_Set_ICCV_2019_paper.pdf) [[code]](https://github.com/qy-feng/margin-openset)

- Separate to Adapt: Open Set Domain Adaptation via Progressive Separation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Separate_to_Adapt_Open_Set_Domain_Adaptation_via_Progressive_Separation_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/thuml/Separate_to_Adapt)

- Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Tan_Weakly_Supervised_Open-Set_Domain_Adaptation_by_Dual-Domain_Collaboration_CVPR_2019_paper.pdf)

- Learning Factorized Representations for Open-set Domain Adaptation [[ICLR2019]](https://openreview.net/pdf?id=SJe3HiC5KX)

- Open Set Domain Adaptation by Backpropagation [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper.pdf) [[Pytorch(Official)]](https://github.com/ksaito-ut/OPDA_BP) [[Tensorflow]](https://github.com/Mid-Push/Open_set_domain_adaptation) [[Pytorch]](https://github.com/YU1ut/openset-DA)

- Open Set Domain Adaptation [[ICCV2017]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Busto_Open_Set_Domain_ICCV_2017_paper.pdf)

**Journal**

- Open-set domain adaptation by deconfounding domain gaps [[Applied Intelligence 2022]](https://link.springer.com/article/10.1007/s10489-022-03805-9)

- Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice [[TPAMI2020]](https://arxiv.org/abs/2002.08681) [[PyTroch]](https://github.com/YBZh/MultiClassDA)

- Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation [[IEEE TMM]](https://arxiv.org/abs/2007.00384) [[Pytorch]](https://github.com/tasfia/DAMC)

**Arxiv**

- Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation [[10 Feb 2020]](https://arxiv.org/abs/2002.03642v1)

- Known-class Aware Self-ensemble for Open Set Domain Adaptation [[3 May 2019]](https://arxiv.org/abs/1905.01068v1)

## Universal DA

**Conference**

- Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP [[EMNLP 2023 Findings]](https://aclanthology.org/2023.findings-emnlp.392/)

- Subsidiary Prototype Alignment for Universal Domain Adaptation [[NeurIPS2022]](https://openreview.net/forum?id=5kThooa07pf) [[Project Page]](https://sites.google.com/view/spa-unida)

- OVANet: One-vs-All Network for Universal Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Saito_OVANet_One-vs-All_Network_for_Universal_Domain_Adaptation_ICCV_2021_paper.html)

- Active Universal Domain Adaptation [[ICCV 2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Ma_Active_Universal_Domain_Adaptation_ICCV_2021_paper.pdf)

- Domain Consensus Clustering for Universal Domain Adaptation [[CVPR 2021]](http://reler.net/papers/guangrui_cvpr2021.pdf) [[Pytorch]](https://github.com/Solacex/Domain-Consensus-Clustering)

- Divergence Optimization for Noisy Universal Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Divergence_Optimization_for_Noisy_Universal_Domain_Adaptation_CVPR_2021_paper.pdf)

- Universal Domain Adaptation through Self Supervision [[NeurIPS 2020]](https://papers.nips.cc/paper/2020/hash/bb7946e7d85c81a9e69fee1cea4a087c-Abstract.html) [[Pytorch]](https://github.com/VisionLearningGroup/DANCE)

- Learning to Detect Open Classes for Universal Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600562.pdf) [[code]](https://github.com/thuml/Calibrated-Multiple-Uncertainties)

- Universal Source-Free Domain Adaptation [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.pdf) [[Project]](https://sites.google.com/view/usfda-cvpr2020)

- Universal Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/You_Universal_Domain_Adaptation_CVPR_2019_paper.pdf)  [[Pytorch]](https://github.com/thuml/Universal-Domain-Adaptation)

**Journal**

- Universal Model Adaptation by Style Augmented Open-set Consistency [[Applied Intelligence 2023]](https://link.springer.com/article/10.1007/s10489-023-04731-0)

**Arxiv**

- Universal Multi-Source Domain Adaptation [[5 Nov 2020]](https://arxiv.org/abs/2011.02594)

- A Sample Selection Approach for Universal Domain Adaptation [[14 Jan 2020]](https://arxiv.org/abs/2001.05071v1)

## Open Compound DA

**Conference**

- Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation [[NeurIPS2020]](https://proceedings.neurips.cc/paper/2020/file/7a9a322cbe0d06a98667fdc5160dc6f8-Paper.pdf)

- Open Compound Domain Adaptation [[CVRP2020 Oral]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Open_Compound_Domain_Adaptation_CVPR_2020_paper.pdf) [[Pytorch]](https://github.com/zhmiao/OpenCompoundDomainAdaptation-OCDA)

**Journal**

- Source-Free Open Compound Domain Adaptation in Semantic Segmentation [[TCSVT 2022]](https://ieeexplore.ieee.org/document/9785619)

## Multi Source DA

**Conference**

- Confident Anchor-Induced Multi-Source Free Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/168908dd3227b8358eababa07fcaf091-Abstract.html) [[code is coming soon]](https://github.com/Learning-group123/CAiDA)

- mDALU: Multi-Source Domain Adaptation and Label Unification With Partial Datasets [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Gong_mDALU_Multi-Source_Domain_Adaptation_and_Label_Unification_With_Partial_Datasets_ICCV_2021_paper.html)

- STEM: An Approach to Multi-Source Domain Adaptation With Guarantees [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Nguyen_STEM_An_Approach_to_Multi-Source_Domain_Adaptation_With_Guarantees_ICCV_2021_paper.html)

- T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Li_T-SVDNet_Exploring_High-Order_Prototypical_Correlations_for_Multi-Source_Domain_Adaptation_ICCV_2021_paper.html)

- Multi-Source Domain Adaptation for Object Detection [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Yao_Multi-Source_Domain_Adaptation_for_Object_Detection_ICCV_2021_paper.html)

- Information-Theoretic Regularization for Multi-Source Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Park_Information-Theoretic_Regularization_for_Multi-Source_Domain_Adaptation_ICCV_2021_paper.html)

- Partial Feature Selection and Alignment for Multi-Source Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf)

- Wasserstein Barycenter for Multi-Source Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf) [[Code]](https://github.com/eddardd/WBTransport)

- Unsupervised Multi-source Domain Adaptation Without Access to Source Data [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper.pdf)

- Dynamic Transfer for Multi-Source Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Transfer_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf) [[Pytorch]](https://github.com/liyunsheng13/DRT)

- Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/He_Multi-Source_Domain_Adaptation_With_Collaborative_Learning_for_Semantic_Segmentation_CVPR_2021_paper.pdf)

- MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning [[UAI2021]](https://auai.org/uai2021/pdf/uai2021.106.pdf)

- Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark [[ICCV Workshop 2021]](https://arxiv.org/abs/2108.10840) [[Pytorch]](https://github.com/bupt-ai-cz/Meta-SelfLearning)

- Your Classifier can Secretly Suffice Multi-Source Domain Adaptation [[NeurIPS 2020]](https://papers.nips.cc/paper/2020/file/3181d59d19e76e902666df5c7821259a-Paper.pdf) [[Project]](https://sites.google.com/view/simpal)

- Multi-Source Open-Set Deep Adversarial Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710732.pdf)

- Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation [[ECCV2020]](https://arxiv.org/abs/2004.04398)

- Multi-Source Open-Set Deep Adversarial Domain Adaptation [[ECCV2020]](https://dipeshtamboli.github.io/blog/2020/Multi-Source-Open-Set-Deep-Adversarial-Domain-Adaptation/)

- Curriculum Manager for Source Selection in Multi-Source Domain Adaptation [[ECCV2020]](https://arxiv.org/abs/2007.01261v1)

- Domain Aggregation Networks for Multi-Source Domain Adaptation [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/6292-Paper.pdf)

- Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation [[ECCV2020]](https://github.com/ChrisAllenMing/LtC-MSDA) [[Pytorch]](https://github.com/ChrisAllenMing/LtC-MSDA)

- Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits [[AAAI2020]](https://arxiv.org/abs/2001.04362v2)

- Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis [[AAAI2020]](https://arxiv.org/pdf/2006.05602.pdf)

- Multi-source Domain Adaptation for Visual Sentiment Classification [[AAAI2020]](https://arxiv.org/abs/2001.03886v1)

- Multi-source Distilling Domain Adaptation [[AAAI2020]](https://arxiv.org/abs/1911.11554v1) [[code]](https://github.com/daoyuan98/MDDA)

- Multi-source Domain Adaptation for Semantic Segmentation [[NeurlPS2019]](https://arxiv.org/abs/1910.12181) [[Pytorch]](https://github.com/Luodian/MADAN)

- Moment Matching for Multi-Source Domain Adaptation [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Peng_Moment_Matching_for_Multi-Source_Domain_Adaptation_ICCV_2019_paper.pdf) [[Pytorch]](http://ai.bu.edu/M3SDA/)

- Multi-Domain Adversarial Learning [[ICLR2019]](https://openreview.net/forum?id=Sklv5iRqYX) [[Torch]](https://github.com/AltschulerWu-Lab/MuLANN)

- Algorithms and Theory for Multiple-Source Adaptation [[NIPS2018]](https://papers.nips.cc/paper/8046-algorithms-and-theory-for-multiple-source-adaptation)

- Adversarial Multiple Source Domain Adaptation [[NIPS2018]](http://papers.nips.cc/paper/8075-adversarial-multiple-source-domain-adaptation) [[Pytorch]](https://github.com/KeiraZhao/MDAN)

- Boosting Domain Adaptation by Discovering Latent Domains [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Mancini_Boosting_Domain_Adaptation_CVPR_2018_paper.pdf) [[Caffe]](https://github.com/mancinimassimiliano/latent_domains_DA) [[Pytorch]](https://github.com/mancinimassimiliano/pytorch_wbn)

- Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift [[CVPR2018]](https://arxiv.org/abs/1803.00830) [[Pytorch]](https://github.com/HCPLab-SYSU/MSDA)

**Journal**

- Graphical Modeling for Multi-Source Domain Adaptation [[TPAMI 2022]](https://ieeexplore.ieee.org/abstract/document/9767755) [[Pytorch]](https://github.com/Francis0625/Graphical-Modeling-for-Multi-Source-Domain-Adaptation)

- Unsupervised sentiment analysis by transferring multi-source knowledge[[Cognitive Computation]](https://arxiv.org/pdf/2105.11902.pdf)

- A survey of multi-source domain adaptation [[Information Fusion]](https://www.sciencedirect.com/science/article/pii/S1566253514001316)

**Arxiv**

- Mutual learning network for multi-source domain adaptation [[arXiv]](https://arxiv.org/pdf/2003.12944)

- Domain Adaptive Ensemble Learning [[arXiv]](https://arxiv.org/abs/2003.07325)

- Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019 [[14 Oct 2019]](https://arxiv.org/abs/1910.03548)

## Multi Target DA

**Conference**

- CoNMix for Source-free Single and Multi-target Domain Adaptation [[WACV2022]](https://openaccess.thecvf.com/content/WACV2023/html/Kumar_CoNMix_for_Source-Free_Single_and_Multi-Target_Domain_Adaptation_WACV_2023_paper.html) [[Pytorch]](https://github.com/vcl-iisc/CoNMix)

- Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation [[CVPR2021]](https://arxiv.org/abs/2104.00808v1) [[Pytorch]](https://openaccess.thecvf.com/content/CVPR2021/papers/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.pdf)

- Multi-Target Domain Adaptation with Collaborative Consistency Learning [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.pdf)

**Arxiv**

- Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach [[arXiv]](https://arxiv.org/abs/1810.11547v1)

## Incremental DA

**Conference**

- Lifelong Domain Adaptation via Consolidated Internal Distribution [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/5caf41d62364d5b41a893adc1a9dd5d4-Abstract.html)

- Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Volpi_Continual_Adaptation_of_Visual_Representations_via_Domain_Randomization_and_Meta-Learning_CVPR_2021_paper.pdf)

- ConDA: Continual Unsupervised Domain Adaptation [[CVPR2021]](https://arxiv.org/abs/2103.11056v1)

- Gradient Regularized Contrastive Learning for Continual Domain Adaptation [[AAAI2021]](https://arxiv.org/abs/2103.12294v1)

- Gradual Domain Adaptation without Indexed Intermediate Domains [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/45017f6511f91be700fda3d118034994-Abstract.html)

- Learning to Adapt to Evolving Domains [[NeurIPS 2020]](https://proceedings.neurips.cc/paper/2020/file/fd69dbe29f156a7ef876a40a94f65599-Paper.pdf) [[Pytorch]](https://github.com/Liuhong99/EAML)

- Class-Incremental Domain Adaptation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580052.pdf)

- Incremental Adversarial Domain Adaptation for Continually Changing Environments [[ICRA2018]](https://arxiv.org/abs/1712.07436)

- Continuous Manifold based Adaptation for Evolving Visual Domains [[CVPR2014]](https://people.eecs.berkeley.edu/~jhoffman/papers/Hoffman_CVPR2014.pdf)

## Multi Step DA

**Arxiv**

- Adversarial Domain Adaptation for Stance Detection [[arXiv]](https://arxiv.org/abs/1902.02401)

- Ensemble Adversarial Training: Attacks and Defenses [[arXiv]](https://arxiv.org/abs/1705.07204)

**Conference**

- Distant domain transfer learning [[AAAI2017]](http://www.ntu.edu.sg/home/sinnopan/publications/[AAAI17]Distant%20Domain%20Transfer%20Learning.pdf)

## Heterogeneous DA

**Conference**

- Domain Adaptive Classification on Heterogeneous Information Networks [[IJCAI2020]](https://www.ijcai.org/Proceedings/2020/0196.pdf)

- Heterogeneous Domain Adaptation via Soft Transfer Network [[ACM MM2019]](https://arxiv.org/abs/1908.10552v1)

## Target-agnostic DA

**Arxiv**

- Compound Domain Adaptation in an Open World [[8 Sep 2019]](https://arxiv.org/abs/1909.03403)

**Conference**

- Domain Agnostic Learning with Disentangled Representations [[ICML2019]](http://proceedings.mlr.press/v97/peng19b/peng19b.pdf) [[Pytorch]](https://github.com/VisionLearningGroup/DAL)

- Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Blending-Target_Domain_Adaptation_by_Adversarial_Meta-Adaptation_Networks_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/zjy526223908/BTDA)

## Federated DA

**Arxiv**

- Federated Adversarial Domain Adaptation [[5 Nov 2019]](https://arxiv.org/abs/1911.02054v1)

## Continuously Indexed DA

**Conference**

- Continuously Indexed Domain Adaptation [[ICML 2020]](http://wanghao.in/paper/ICML20_CIDA.pdf) [[Pytorch]](https://github.com/hehaodele/CIDA) [[Project Page]](https://github.com/hehaodele/CIDA/blob/master/README.md) [[Video]](https://www.youtube.com/watch?v=KtZPSCD-WhQ)

## Source Free DA

**Conference**

- Domain Adaptation with Adversarial Training on Penultimate Activations [[AAAI2023]](https://ojs.aaai.org/index.php/AAAI/article/view/26185) [[Pytorch]](https://github.com/tsun/APA)

- Source-free Domain Adaptive Human Pose Estimation [[ICCV2023]](https://arxiv.org/abs/2308.03202)[[Pytorch]](https://github.com/davidpengucf/SFDAHPE)

- RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation [[IJCAI2023]](https://www.ijcai.org/proceedings/2023/458) [[Pytorch]](https://github.com/davidpengucf/RAIN)

- CoNMix for Source-free Single and Multi-target Domain Adaptation [[WACV2022]](https://openaccess.thecvf.com/content/WACV2023/html/Kumar_CoNMix_for_Source-Free_Single_and_Multi-Target_Domain_Adaptation_WACV_2023_paper.html) [[Pytorch]](https://github.com/vcl-iisc/CoNMix)

- Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition [[ECCV2022]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136940144.pdf) [[Pytorch]](https://github.com/xuyu0010/ATCoN) [[Project]](https://xuyu0010.github.io/sfvda.html)

- Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation [[ECCV2022]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/912_ECCV_2022_paper.php) [[Project Page]](https://sites.google.com/view/sticker-sfda)

- Balancing Discriminability and Transferability for Source-Free Domain Adaptation [[ICML2022]](https://proceedings.mlr.press/v162/kundu22a.html) [[Project Page]](https://sites.google.com/view/mixup-sfda)

- Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation [[IJCAI2021]](https://arxiv.org/abs/2106.15326) [[Pytorch]](https://github.com/SCUT-AILab/CPGA)

- Confident Anchor-Induced Multi-Source Free Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/168908dd3227b8358eababa07fcaf091-Abstract.html) [[Pytorch]](https://github.com/Learning-group123/CAiDA)

- Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/1dba5eed8838571e1c80af145184e515-Abstract.html) [[Pytorch]](https://github.com/jxhuang0508/HCL)

- Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/f5deaeeae1538fb6c45901d524ee2f98-Abstract.html) [[Pytorch]](https://github.com/Albert0147/SFDA_neighbors)

- Unsupervised Domain Adaptation of Black-Box Source Models [[BMVC2021]](https://www.bmvc2021-virtualconference.com/assets/papers/0404.pdf)[[Pytorch]](https://github.com/zhjscut/IterLNL)

- Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Kundu_Generalize_Then_Adapt_Source-Free_Domain_Adaptive_Semantic_Segmentation_ICCV_2021_paper.html) [[Project]](https://sites.google.com/view/sfdaseg)

- Generalized Source-free Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Generalized_Source-Free_Domain_Adaptation_ICCV_2021_paper.pdf) [[Pytorch]](https://github.com/Albert0147/G-SFDA)

- Adaptive Adversarial Network for Source-free Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Xia_Adaptive_Adversarial_Network_for_Source-Free_Domain_Adaptation_ICCV_2021_paper.pdf) [[Pytorch]](https://github.com/HaifengXia/SFDA)

- Visualizing Adapted Knowledge in Domain Transfer [[CVPR2021]](https://arxiv.org/abs/2104.10602) [[Pytorch]](https://github.com/hou-yz/DA_visualization)

- Unsupervised Multi-source Domain Adaptation Without Access to Source Data [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper.pdf) [[Pytorch]](https://github.com/driptaRC/DECISION)

- Source-Free Domain Adaptation for Semantic Segmentation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.pdf)

- Domain Impression: A Source Data Free Domain Adaptation Method [[WACV2021]](https://openaccess.thecvf.com/content/WACV2021/papers/Kurmi_Domain_Impression_A_Source_Data_Free_Domain_Adaptation_Method_WACV_2021_paper.pdf) [[Project]](https://delta-lab-iitk.github.io/SFDA/)

- Model Adaptation: Unsupervised Domain Adaptation Without Source Data [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Model_Adaptation_Unsupervised_Domain_Adaptation_Without_Source_Data_CVPR_2020_paper.pdf)

- Universal Source-Free Domain Adaptation [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.pdf) [[Project]](https://sites.google.com/view/usfda-cvpr2020)

- Towards Inheritable Models for Open-Set Domain Adaptation [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Kundu_Towards_Inheritable_Models_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.pdf) [[Project]](https://sites.google.com/view/inheritune)

- Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [[ICML2020]](http://proceedings.mlr.press/v119/ishida20a.html) [[Pytorch]](https://github.com/tim-learn/SHOT)

**Arxiv**

- Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer[[7 Jul 2021]](https://arxiv.org/abs/2107.03008)[[Pytorch]](https://github.com/Wang-xd1899/SSHT)

- Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer [[14 Dec 2020]](https://arxiv.org/abs/2012.07297) [[Pytorch]](https://github.com/tim-learn/SHOT-plus)

## Active DA

**Conference**

- Local Context-Aware Active Domain Adaptation [[ICCV2023]](https://arxiv.org/abs/2208.12856) [[Pytorch]](https://github.com/tsun/LADA)

- Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift [[WACV2023]](https://openaccess.thecvf.com/content/WACV2023/papers/Agarwal_Reducing_Annotation_Effort_by_Identifying_and_Labeling_Contextually_Diverse_Classes_WACV_2023_paper.pdf)

- Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.pdf)[[Pytorch]](https://github.com/BIT-DA/RIPU)

- Active Learning for Domain Adaptation: An Energy-based Approach [[AAAI2022]](ttps://arxiv.org/abs/2112.01406)[[Pytorch]](https://github.com/BIT-DA/EADA)

- Multi-Anchor Active Domain Adaptation for Semantic Segmentation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Ning_Multi-Anchor_Active_Domain_Adaptation_for_Semantic_Segmentation_ICCV_2021_paper.html)

- Active Domain Adaptation via Clustering Uncertainty-Weighted Embeddings [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Prabhu_Active_Domain_Adaptation_via_Clustering_Uncertainty-Weighted_Embeddings_ICCV_2021_paper.html)

- Active Universal Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Ma_Active_Universal_Domain_Adaptation_ICCV_2021_paper.html)

- S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Rangwani_S3VAADA_Submodular_Subset_Selection_for_Virtual_Adversarial_Active_Domain_Adaptation_ICCV_2021_paper.pdf)

- Transferable Query Selection for Active Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Transferable_Query_Selection_for_Active_Domain_Adaptation_CVPR_2021_paper.pdf)

## Generalized Domain Adaptation

**Conference**

- Generalized Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Mitsuzumi_Generalized_Domain_Adaptation_CVPR_2021_paper.pdf)

## Model Selection

- Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation [[ICLR2023ORAL]](https://openreview.net/forum?id=M95oDwJXayG) [[Pytorch]](https://github.com/Xpitfire/iwa)

- The Balancing Principle for Parameter Choice in Distance-Regularized Domain Adaptation [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/ae0909a324fb2530e205e52d40266418-Abstract.html) [[Pytorch]](https://github.com/xpitfire/bpda)

- Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation [[ICML2019]](http://proceedings.mlr.press/v97/you19a/you19a.pdf) [[Pytorch]](https://github.com/thuml/Deep-Embedded-Validation)

## Other Transfer Learning Paradigms

### Domain Generalization

**Conference**

- Adapting to Distribution Shift by Visual Domain Prompt Generation [[ICLR2024]](https://arxiv.org/pdf/2405.02797) [[Pytorch]](https://github.com/Guliisgreat/VDPG)

- Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization [[AAAI2024 (Oral)]](https://arxiv.org/pdf/2312.10165)

- A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation [[CVPR 2024]](https://arxiv.org/abs/2403.11310) [[Pytorch]](https://github.com/davidpengucf/DAF-DG)

- Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment [[CVPR 2024]](https://openaccess.thecvf.com/content/CVPR2024/papers/Danish_Improving_Single_Domain-Generalized_Object_Detection_A_Focus_on_Diversification_and_CVPR_2024_paper.pdf) [[Pytorch]](https://github.com/msohaildanish/DivAlign)

- Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation [[WACV 2024]](https://arxiv.org/abs/2312.01850) [[Pytorch]](https://github.com/JNiemeijer/DIDEX)

- Topology-aware Robust Optimization for Out-of-Distribution Generalization [[ICLR 2023]](https://arxiv.org/pdf/2307.13943) [[Pytorch]](https://github.com/joffery/TRO)

- A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation [[ICCV Workshop 2023]](https://arxiv.org/abs/2308.13331) [[Pytorch]](https://github.com/ifnspaml/revt-domain-generalization)

- Weight Averaging Improves Knowledge Distillation under Domain Shift [[ICCV Workshop 2023]](https://arxiv.org/abs/2309.11446) [[Pytorch]](https://github.com/vorobeevich/distillation-in-dg)

- Adaptive Texture Filtering for Single-Domain Generalized Segmentation [[AAAI2023 oral]](https://arxiv.org/abs/2303.02943) [[Pytorch]](https://github.com/leelxh/Adaptive-Texture-Filtering-for-Single-Domain-Generalized-Segmentation)

- PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization [[ICCV2023]](https://arxiv.org/abs/2307.15199) [[Project]](https://promptstyler.github.io/)

- Sparse Mixture-of-Experts are Domain Generalizable Learners [[ICLR2023(Oral)]](https://openreview.net/forum?id=RecZ9nB9Q4) [[Pytorch]](https://github.com/Luodian/Generalizable-Mixture-of-Experts)

- Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts [[NeruIPS2022]](https://arxiv.org/pdf/2210.03885.pdf) [[Pytorch]](https://github.com/n3il666/Meta-DMoE)

- Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation [[ECCV 2022]](https://arxiv.org/pdf/2204.02548.pdf) [[Pytorch]](https://github.com/HeliosZhao/SHADE)

- Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification [[CVPR 2021]](https://arxiv.org/pdf/2012.00417.pdf) [[Pytorch]](https://github.com/HeliosZhao/M3L)

- Domain Generalization via Inference-time Label-Preserving Target Projections [[CVPR2021]](https://arxiv.org/abs/2103.01134) [[Pytorch]](https://github.com/peterDan8/InferenceTimeDG)

- Domain Generalization via Entropy Regularization [[NeurIPS2020]](https://papers.nips.cc/paper/2020/file/b98249b38337c5088bbc660d8f872d6a-Paper.pdf) [[Pytorch]](https://github.com/sshan-zhao/DG_via_ER)

- Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [[NeurIPS2020]](https://arxiv.org/abs/2009.12829)

- Learning to Learn with Variational Information Bottleneck for Domain Generalization [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550205.pdf)

- Self-Challenging Improves Cross-Domain Generalization [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470120.pdf) [[Pytorch]](https://github.com/DeLightCMU/RSC)

- Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540154.pdf) [[Pytorch]](https://github.com/emma-sjwang/EISNet)

- Learning to Balance Specificity and Invariance for In and Out of Domain Generalization [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540290.pdf) [[Pytorch]](https://github.com/prithv1/DMG)

- Learning to Generate Novel Domains for Domain Generalization [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610545.pdf)

- Learning to Optimize Domain Specific Normalization for Domain Generalization [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670069.pdf)

- Towards Recognizing Unseen Categories in Unseen Domains [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680460.pdf) [[Pytorch]](https://github.com/mancinimassimiliano/CuMix)

- Efficient Domain Generalization via Common-Specific Low-Rank Decomposition [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/4649-Paper.pdf) [[Pytorch]](https://github.com/vihari/csd)

- Learning to Learn Single Domain Generalization [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qiao_Learning_to_Learn_Single_Domain_Generalization_CVPR_2020_paper.pdf) [[Pytorch]](https://github.com/joffery/M-ADA)

- Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition [[ICLR2020]](https://openreview.net/forum?id=H1lxVyStPH)

- Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation [[ICLR2020]](https://openreview.net/forum?id=SJl5Np4tPr)

- Domain Generalization Using a Mixture of Multiple Latent Domains [[AAAI2020]](https://arxiv.org/abs/1911.07661v1) [[Pytorch]](https://github.com/mil-tokyo/dg_mmld)

- Deep Domain-Adversarial Image Generation for Domain Generalisation [[Paper]](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-ZhouK.2138.pdf) [[Pytorch]](https://github.com/KaiyangZhou/Dassl.pytorch)

- Domain Generalization via Model-Agnostic Learning of Semantic Features [[NeurIPS2019]](https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features) [[Tensorflow]](https://github.com/biomedia-mira/masf)

- Episodic Training for Domain Generalization [[ICCV2019 Oral]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Episodic_Training_for_Domain_Generalization_ICCV_2019_paper.pdf) [Pytorch]](https://github.com/HAHA-DL/Episodic-DG)

- Feature-Critic Networks for Heterogeneous Domain Generalization [[ICML2019]](http://proceedings.mlr.press/v97/li19l/li19l.pdf) [[Pytorch]](https://github.com/liyiying/Feature_Critic)

- Domain Generalization by Solving Jigsaw Puzzles [[CVPR2019 Oral]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Carlucci_Domain_Generalization_by_Solving_Jigsaw_Puzzles_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/fmcarlucci/JigenDG)

- MetaReg: Towards Domain Generalization using Meta-Regularization [[NIPS2018]](https://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization)

- Deep Domain Generalization via Conditional Invariant Adversarial Networks [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf)

- Domain Generalization with Adversarial Feature Learning [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Domain_Generalization_With_CVPR_2018_paper.pdf)

**Journal**

- Domain Generalization for Regression [[IntellManuf2020]](https://link.springer.com/article/10.1007/s10845-019-01499-4)

- Correlation-aware Adversarial Domain Adaptation and Generalization [[Pattern Recognition(2019)]](https://arxiv.org/abs/1911.12983v1) [[code]](https://github.com/mahfujur1/CA-DA-DG)

**Arxiv**

- Adversarial Pyramid Network for Video Domain Generalization [[8 Dec 2019]](https://arxiv.org/abs/1912.03716)

- Towards Shape Biased Unsupervised Representation Learning for Domain Generalization [[18 Sep 2019]](https://arxiv.org/abs/1909.08245v1)

- A Generalization Error Bound for Multi-class Domain Generalization [[24 May 2019]](https://arxiv.org/abs/1905.10392v1)

- Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization [[29 Apr 2019]](https://arxiv.org/abs/1904.12543v1)

- Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models [[9 Dec 2018]](https://arxiv.org/abs/1812.03407v1)

### Domain Randomization

**Conference**

- DeceptionNet: Network-Driven Domain Randomization [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zakharov_DeceptionNet_Network-Driven_Domain_Randomization_ICCV_2019_paper.pdf)

- Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yue_Domain_Randomization_and_Pyramid_Consistency_Simulation-to-Real_Generalization_Without_Accessing_Target_ICCV_2019_paper.pdf)

### Transfer Metric Learning

- Transfer Metric Learning: Algorithms, Applications and Outlooks [[arXiv]](https://arxiv.org/abs/1810.03944)

### Knowledge Transfer

**Conference**

- Attention Bridging Network for Knowledge Transfer [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Attention_Bridging_Network_for_Knowledge_Transfer_ICCV_2019_paper.pdf)

- Few-Shot Image Recognition with Knowledge Transfer [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Peng_Few-Shot_Image_Recognition_With_Knowledge_Transfer_ICCV_2019_paper.pdf)

### Others

**Conference**

- Learning Across Tasks and Domains [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Ramirez_Learning_Across_Tasks_and_Domains_ICCV_2019_paper.pdf)

- UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation [[ICCV2019]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Kundu_UM-Adapt_Unsupervised_Multi-Task_Adaptation_Using_Adversarial_Cross-Task_Distillation_ICCV_2019_paper.pdf)

- Domain Agnostic Learning with Disentangled Representations [[ICML2019]](https://arxiv.org/abs/1904.12347v1)

- Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization [[CVPR2019]](https://arxiv.org/abs/1904.08631) [[Pytorch]](https://github.com/junbaoZHUO/UODTN)

**Arxiv**

- GradMix: Multi-source Transfer across Domains and Tasks [[9 Feb 2020]](GradMix: Multi-source Transfer across Domains and Tasks)

- When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets [[arXiv 13 Dec 2018]](https://arxiv.org/abs/1812.05313)

## Applications

### Object Detection

**Survey**

- Unsupervised Domain Adaptation of Object Detectors: A Survey [[Arxiv 27 May 2021]](https://arxiv.org/abs/2105.13502)

  

**Conference**

- Improving Object Detection via Local-Global Contrastive Learning [[BMVC2024]](https://arxiv.org/abs/2410.05058) [[Project]](https://local-global-detection.github.io/)

- Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond [[AAAI2024]](https://arxiv.org/abs/2303.10343)

- Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection [[CVPR2023]](https://arxiv.org/abs/2203.15793) [[Project]](https://viudomain.github.io/irg-sfda-web/)

- Towards Online Domain Adaptive Object Detection [[WACV2023]](https://arxiv.org/abs/2204.05289) [[https://github.com/Vibashan/online-da]]

- Mixture of Teacher Experts for Source-Free Domain Adaptive Object Detection [[ICIP2022]](https://ieeexplore.ieee.org/document/9897795)

- Towards Robust Adaptive Object Detection under Noisy Annotations [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Robust_Adaptive_Object_Detection_Under_Noisy_Annotations_CVPR_2022_paper.pdf) [[PyTorch]](https://github.com/CityU-AIM-Group/NLTE)

- H²FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.pdf) [[PyTorch]](https://github.com/XuYunqiu/H2FA_R-CNN) [[PaddlePaddle]](https://github.com/XuYunqiu/H2FA_R-CNN/tree/ppdet)

- Cross-Domain Adaptive Teacher for Object Detection [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Cross-Domain_Adaptive_Teacher_for_Object_Detection_CVPR_2022_paper.pdf) [[Project]](https://yujheli.github.io/projects/adaptiveteacher.html) [[PyTorch]](https://github.c
ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

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https://github.com/zhaoxin94/awesome-domain-adaptation

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