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awesome-transfer-learning
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
https://github.com/artix41/awesome-transfer-learning
Last synced: 3 days ago
JSON representation
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Surveys
- A Survey on Transfer Learning
- Transfer Learning for Reinforcement Learning Domains: A Survey
- A Survey of transfer learning
- Domain Adaptation for Visual Applications: A Comprehensive Survey
- Deep Visual Domain Adaptation: A Survey
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- Transfer Learning for Reinforcement Learning Domains: A Survey
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
- A Survey of transfer learning
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Deep Transfer Learning
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Fine-tuning approach
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Feature extraction (embedding) approach
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Multi-task learning
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Policy transfer for RL
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Few-shot transfer learning
- Zero-Shot Transfer Learning for Event Extraction
- Learning a Deep Embedding Model for Zero-Shot Learning
- Zero-Shot Object Detection
- LSTD: A Low-Shot Transfer Detector for Object Detection
- Multidomain Document Layout Understanding using Few Shot Object Detection
- One-Shot Unsupervised Cross Domain Translation
- Multidomain Document Layout Understanding using Few Shot Object Detection
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Meta transfer learning
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Applications
- Deep Convolutional Neural Networks forComputer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
- Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
- Comparison of deep transfer learning strategies for digital pathology
- A Deep Convolutional Neural Network for Location Recognition and Geometry Based Information
- Multiresolution Knowledge Distillation for Anomaly Detection
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Unsupervised Domain Adaptation
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Theory
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Adversarial methods
- Domain-Adversarial Training of Neural Networks
- Deep Transfer Learning with Joint Adaptation Networks
- Coupled Generative Adversarial Networks
- Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
- Domain Separation Networks
- Adaptative Discriminative Domain Adaptation
- Generate To Adapt: Aligning Domains using Generative Adversarial Networks
- Wasserstein Distance Guided Representation Learning for Domain Adaptation
- CyCADA: Cycle-Consistent Adversarial Domain Adaptation
- A DIRT-T Approach to Unsupervised Domain Adaptation
- Duplex Generative Adversarial Network for Unsupervised Domain Adaptation
- Learning Semantic Representations for Unsupervised Domain Adaptation
- Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
- Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
- Incremental Unsupervised Domain-Adversarial Training of Neural Networks
- Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
- Deep Identity-aware Transfer of Facial Attributes
- Image-to-Image Translation with Conditional Adversarial Networks
- Unsupervised Cross-domain Image Generation
- Learning from Simulated and Unsupervised Images through Adversarial Training (2016)
- Unsupervised Image-to-Image Translation Networks
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
- From source to target and back: symmetric bi-directional adaptive GAN
- One-Sided Unsupervised Domain Mapping
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- Image to Image Translation for Domain Adaptation
- Multimodal Unsupervised Image-to-Image Translation
- Unsupervised Latent Space Translation Network
- StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
- XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
- Toward Multimodal Image-to-Image Translation
- Label Efficient Learning of Transferable Representations across Domains and Tasks
- ComboGAN: Unrestrained Scalability for Image Domain Translation
- Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
- RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
- Multi-Adversarial Domain Adaptation
- Multiple Source Domain Adaptation with Adversarial Learning
- Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
- Recycle-GAN: Unsupervised Video Retargeting
- Video-to-Video Synthesis
- Everybody Dance Now
- Temporal Attentive Alignment for Large-Scale Video Domain Adaptation
- Video-to-Video Synthesis
- Everybody Dance Now
- Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
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Optimal Transport
- Optimal Transport for Domain Adaptation
- Theoretical Analysis of Domain Adaptation with Optimal Transport
- Joint distribution optimal transportation for domain adaptation
- Large Scale Optimal Transport and Mapping Estimation
- Optimal Transport for Multi-source Domain Adaptation under Target Shift
- DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation
- Joint distribution optimal transportation for domain adaptation
- Large Scale Optimal Transport and Mapping Estimation
- DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation
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Embedding methods
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Kernel methods
- Covariate Shift in Hilbert Space: A Solution via Surrogate Kernels
- Learning Transferable Features with Deep Adaptation Networks
- Unsupervised Domain Adaptation with Residual Transfer Networks
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
- A Simple Approach for Unsupervised Domain Adaptation
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Autoencoder approach
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Subspace Learning
- Domain Adaptation for Object Recognition: An Unsupervised Approach
- Geodesic Flow Kernel for Unsupervised Domain Adaptation
- Unsupervised Visual Domain Adaptation Using Subspace Alignment
- Return of Frustratingly Easy Domain Adaptation
- Deep CORAL: Correlation Alignment for Deep Domain Adaptation
- Learning an Invariant Hilbert Space for Domain Adaptation
- Correlation Alignment by Riemannian Metric for Domain Adaptation
- A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
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Self-Ensembling methods
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Other
- Adapting Visual Category Models to New Domains
- Revisiting Batch Normalization for Practical Domain Adaptation
- AutoDIAL: Automatic Domain Alignment Layers
- Fully Test-time Adaptation by Entropy Minimization
- Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
- Improving robustness against common corruptions by covariate shift adaptation
- Instance Adaptive Self-Training for Unsupervised Domain Adaptation
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Digits transfer (unsupervised)
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Audio Processing
- Open AI Retro Contest
- Transfer Learning in Action - learning-in-action)
- Transfer Learning for Natural Language Processing
- Hands-On Transfer Learning with Python - on-transfer-learning-with-python)
- Visual Domain Adaptation Challenge (VisDA)
- Hands-On Transfer Learning with Python - on-transfer-learning-with-python)
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Semi-supervised Domain Adaptation
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General methods
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Subspace learning
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Copulas methods
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Few-shot Supervised Domain Adaptation
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Adversarial methods
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Embedding methods
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Applied Domain Adaptation
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Physics
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Audio Processing
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation
- Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
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Image-to-image
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Audio Processing
- MNIST - M](https://drive.google.com/file/d/0B9Z4d7lAwbnTNDdNeFlERWRGNVk/view) vs [SVHN](http://ufldl.stanford.edu/housenumbers/) vs [Synth](https://drive.google.com/file/d/0B9Z4d7lAwbnTSVR1dEFSRUFxOUU/view) vs [USPS](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps): digit images
- GTSRB
- NYU Depth Dataset V2
- CelebA
- Office-Caltech dataset - 31 and Caltech-256 datasets. There are in total four domains: Amazon, Webcam, DSLR and Caltech.
- Cityscapes dataset
- UnityEyes - inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild-mpiigaze/): simulated vs real gaze images (eyes)
- CycleGAN datasets
- pix2pix dataset
- RaFD
- Office-Home dataset - world.
- DukeMTMC-reid - 1501](http://www.liangzheng.org/Project/project_reid.html): two pedestrian datasets collected at different places. The evaluation metric is based on open-set image retrieval.
- NYU Depth Dataset V2
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Text-to-text
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Audio Processing
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Other
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Audio Processing
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Categories
Sub Categories
Adversarial methods
49
Audio Processing
37
Kernel methods
15
Optimal Transport
9
Subspace Learning
8
Other
7
Few-shot transfer learning
7
Applications
5
Theory
5
Fine-tuning approach
4
Embedding methods
3
Physics
3
Subspace learning
2
Feature extraction (embedding) approach
2
Autoencoder approach
2
Self-Ensembling methods
1
Multi-task learning
1
Meta transfer learning
1
Copulas methods
1
General methods
1
Policy transfer for RL
1