{"id":43150,"url":"https://github.com/weitianxin/awesome-distribution-shift","name":"awesome-distribution-shift","description":"A curated list of papers and resources about the distribution shift in machine learning.","projects_count":204,"last_synced_at":"2026-10-18T16:00:22.110Z","repository":{"id":170849108,"uuid":"579943426","full_name":"weitianxin/awesome-distribution-shift","owner":"weitianxin","description":"A curated list of papers and resources about the distribution shift in machine learning.","archived":false,"fork":false,"pushed_at":"2023-08-05T22:06:33.000Z","size":607,"stargazers_count":131,"open_issues_count":1,"forks_count":9,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-09-29T05:10:46.363Z","etag":null,"topics":["artificial-intelligence","deep-learning","distribution-shift","domain-adaptation","domain-generalization","fairness","generalization","graph","machine-learning","neural-network","robustness"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/weitianxin.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2022-12-19T10:30:03.000Z","updated_at":"2026-09-15T05:17:18.000Z","dependencies_parsed_at":"2023-12-30T09:31:39.430Z","dependency_job_id":null,"html_url":"https://github.com/weitianxin/awesome-distribution-shift","commit_stats":null,"previous_names":["weitianxin/awesome-distribution-shift"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/weitianxin/awesome-distribution-shift","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weitianxin%2Fawesome-distribution-shift","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weitianxin%2Fawesome-distribution-shift/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weitianxin%2Fawesome-distribution-shift/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weitianxin%2Fawesome-distribution-shift/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/weitianxin","download_url":"https://codeload.github.com/weitianxin/awesome-distribution-shift/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weitianxin%2Fawesome-distribution-shift/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":344872684,"owners_count":38334908,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-10-03T21:59:58.778Z","status":"online","status_checked_at":"2026-10-17T02:00:06.547Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T21:18:32.850Z","updated_at":"2026-10-18T16:00:22.119Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Generalization","Detection","Data Modality","Learning Strategy","Benchmark","Decentralized (Federated)","Fairness","Robustness"],"sub_categories":["Domain Generalization \u0026 Out-of-distribution Robustness","Domain Adaptation","Others (RecSys)","Video","Text","Test-time Adaptation/Training","Graph","Time Series","Speech","Tabular Data"],"readme":"# awesome-distribution-shift ![PRs Welcome](https://img.shields.io/badge/PRs-Welcome-green) ![Awesome](https://awesome.re/badge.svg)\n\nA curated list of papers and resources about the distribution shift in machine learning. I categorize them based on their topic and content. I will try to make this list updated.\n\nHere is an example of distribution shift in images across domains from [DomainBed](https://github.com/facebookresearch/DomainBed).\n\n![avatar](https://github.com/weitianxin/awesome-distribution-shift/blob/main/figs/example.png)\n\nI categorize the papers on distribution shift as follows. If you found any error or any missed paper, please don't hesitate to add.\n\nTo be updated\n\n## Contents\n- [awesome-distribution-shift  ](#awesome-distribution-shift--)\n  - [Contents](#contents)\n  - [Benchmark](#benchmark)\n  - [Generalization](#generalization)\n    - [Domain Generalization \\\u0026 Out-of-distribution Robustness](#domain-generalization--out-of-distribution-robustness)\n      - [Out-of-distribution Robustness](#out-of-distribution-robustness)\n      - [Domain Generalization](#domain-generalization)\n    - [Domain Adaptation](#domain-adaptation)\n    - [Test-time Adaptation/Training](#test-time-adaptationtraining)\n  - [Data Modality](#data-modality)\n    - [Graph](#graph)\n    - [Text](#text)\n    - [Time Series](#time-series)\n    - [Video](#video)\n    - [Speech](#speech)\n    - [Tabular Data](#tabular-data)\n    - [Others (RecSys)](#others-recsys)\n  - [Decentralized (Federated)](#decentralized-federated)\n  - [Detection](#detection)\n  - [Fairness](#fairness)\n  - [Robustness](#robustness)\n  - [Learning Strategy](#learning-strategy)\n\n## Benchmark\n\n- [ICLR 2021] **In Search of Lost Domain Generalization** [[paper]](https://arxiv.org/abs/2007.01434) [[code (DomainBed)]](https://github.com/facebookresearch/DomainBed)\n- [ICLR 2021] **BREEDS: Benchmarks for Subpopulation Shift** [[paper]](https://arxiv.org/abs/2008.04859) [[code]](https://github.com/MadryLab/BREEDS-Benchmarks)\n- [ICML 2021] **WILDS: A Benchmark of in-the-Wild Distribution Shifts** [[paper]](https://arxiv.org/abs/2012.07421) [[code]](https://github.com/p-lambda/wilds)\n- [NeurIPS 2021] **Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks** [[paper]](https://openreview.net/pdf?id=qM45LHaWM6E) [[code]](https://github.com/Shifts-Project/shifts)\n- [ICLR 2022] **MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts** [[paper]](https://openreview.net/pdf?id=MTex8qKavoS) [[code]](https://metashift.readthedocs.io/en/latest/)\n- [NeurIPS 2022] **GOOD: A Graph Out-of-Distribution Benchmark** [[paper]](https://arxiv.org/abs/2206.08452) [[code]](https://github.com/divelab/GOOD)\n- [NeurIPS 2022] **BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs** [[paper]](https://arxiv.org/abs/2206.10071) [[code]](https://github.com/pygod-team/pygod/tree/main/benchmark)\n- [NeurIPS 2022] **ADBench: Anomaly Detection Benchmark** [[paper]](https://arxiv.org/abs/2206.09426) [[code]](https://github.com/Minqi824/ADBench)\n- [NeurIPS 2022] **Wild-Time: A Benchmark of in-the-Wild Distribution Shifts over Time** [[paper]](https://arxiv.org/abs/2211.14238) [[code]](https://github.com/huaxiuyao/Wild-Time)\n- [NeurIPS 2022] **OpenOOD: Benchmarking Generalized Out-of-Distribution Detection** [[paper]](https://openreview.net/pdf?id=gT6j4_tskUt) [[code]](https://github.com/Jingkang50/OpenOOD)\n- [NeurIPS 2022] **AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection** [[paper]](https://arxiv.org/abs/2206.15476) [[code]](https://github.com/bit-ml/anoshift)\n- [CVPR 2022] **OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization** [[paper]](https://arxiv.org/abs/2106.03721) [[code]](https://github.com/m-Just/OoD-Bench)\n- [CVPR 2022] **The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.pdf) [[code]](https://google.github.io/auto-arborist/)\n- [ECCV 2022] **OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images** [[paper]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136680158.pdf) [[code]](https://github.com/eccv22-ood-workshop/ROBIN-dataset)\n- [arxiv 2022] **DrugOOD: OOD Dataset Curator and Benchmark for AI Aided Drug Discovery** [[paper]](https://arxiv.org/abs/2201.09637) [[code]](https://github.com/tencent-ailab/DrugOOD)\n\n## Generalization\n\n### Domain Generalization \u0026 Out-of-distribution Robustness\n\nThere are mainly two types of distribution shift: domain shift (testing on unseen domains) and subpopulation shift (the domains of testing data are seen but underrepresented in the training data). Below figure from [GOOD](https://github.com/divelab/GOOD) well demonstrates them.\n![avatar](https://github.com/weitianxin/awesome-distribution-shift/blob/main/figs/dis%20shift.png)\n**Domain Generalization** mainly studies **domain shift**, while **Out-of-distribution Robustness** studies **both** of them. These two research directions are very related and share a lot in common.\n\n#### Out-of-distribution Robustness\n\n- [arxiv 2019] **Invariant Risk Minimization** [[paper]](https://arxiv.org/abs/1907.02893)\n- [ICLR 2020] **Distributionally Robust Neural Networks for Group Shifts: on the Importance of Regularization for Worst-Case Generalization** [[paper]](https://openreview.net/pdf?id=ryxGuJrFvS)\n- [ICLR 2021] **In-N-Out: Pre-Training and Self-Training Using Auxiliary Information for Out-of-Distribution Robustness** [[paper]](https://arxiv.org/pdf/2012.04550)\n- [ICML 2021] **Out-of-Distribution Generalization Via Risk Extrapolation (REx)** [[paper]](http://proceedings.mlr.press/v139/krueger21a/krueger21a.pdf)\n- [ICML 2021] **Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and in-Distribution Generalization** [[paper]](https://proceedings.mlr.press/v139/miller21b/miller21b.pdf)\n- [NeurIPS 2021] **Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization** [[paper]](https://openreview.net/pdf?id=jlchsFOLfeF)\n- [ICML 2022] **Improving Out-of-Distribution Robustness Via Selective Augmentation** [[paper]](https://arxiv.org/pdf/2201.00299)\n- [ICML 2022] **Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization** [[paper]](https://arxiv.org/pdf/2109.02934)\n- [NeurIPS 2022] **Diverse Weight Averaging for Out-of-Distribution Generalization** [[paper]](https://arxiv.org/pdf/2205.09739)\n\n**W/o group label**\n\n- [ICML 2021] **Just Train Twice: Improving Group Robustness Without Training Group Information** [[paper]](https://arxiv.org/pdf/2107.09044)\n- [ICML 2022] **Model Agnostic Sample Reweighting for Out-of-Distribution Learning** [[paper]](https://proceedings.mlr.press/v162/zhou22d/zhou22d.pdf)\n- [ICML 2022] **Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations** [[paper]](https://arxiv.org/pdf/2203.01517)\n\n#### Domain Generalization\n\n- [AAAI 2018] **Learning to Generalize: Meta-Learning for Domain Generalization** [[paper]](https://arxiv.org/pdf/1710.03463)\n- [CVPR 2018] **Domain Generalization With Adversarial Feature Learning** [[paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Domain_Generalization_With_CVPR_2018_paper.pdf)\n- [ECCV 2018] **Deep Domain Generalization Via Conditional Invariant Adversarial Networks** [[paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf)\n- [NeurIPS 2018] **MetaReg: Towards Domain Generalization Using Meta-Regularization** [[paper]](https://papers.nips.cc/paper/2018/file/647bba344396e7c8170902bcf2e15551-Paper.pdf)\n- [NeurIPS 2019] **Domain Generalization Via Model-Agnostic Learning of Semantic Features** [[paper]](https://proceedings.neurips.cc/paper/2019/file/2974788b53f73e7950e8aa49f3a306db-Paper.pdf)\n- [CVPR 2019] **Episodic Training for Domain Generalization** [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Episodic_Training_for_Domain_Generalization_ICCV_2019_paper.pdf)\n- [CVPR 2019] **Domain Generalization By Solving Jigsaw Puzzles** [[paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Carlucci_Domain_Generalization_by_Solving_Jigsaw_Puzzles_CVPR_2019_paper.pdf)\n- [NeurIPS 2020] **Domain Generalization Via Entropy Regularization** [[paper]](https://proceedings.neurips.cc/paper/2020/file/b98249b38337c5088bbc660d8f872d6a-Paper.pdf)\n- [ECCV 2020] **Learning to Optimize Domain Specific Normalization for Domain Generalization** [[paper]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670069.pdf)\n- [ECCV 2020] **Learning From Extrinsic and Intrinsic Supervisions for Domain Generalization** [[paper]](https://arxiv.org/pdf/2007.09316)\n- [ECCV 2020] **Learning to Balance Specificity and Invariance for in and Out of Domain Generalization** [[paper]](https://arxiv.org/pdf/2008.12839)\n- [ECCV 2020] **Self-Challenging Improves Cross-Domain Generalization** [[paper]](https://arxiv.org/pdf/2007.02454)\n- [ECCV 2020] **Learning to Generate Novel Domains for Domain Generalization** [[paper]](https://arxiv.org/pdf/2007.03304)\n- [CVPR 2020] **Learning to Learn Single Domain Generalization** [[paper]](https://arxiv.org/pdf/2003.13216)\n- [JMLR 2021] **Domain Generalization By Marginal Transfer Learning** [[paper]](https://www.jmlr.org/papers/volume22/17-679/17-679.pdf)\n- [NeurIPS 2021] **Swad: Domain Generalization By Seeking Flat Minima** [[paper]](https://proceedings.neurips.cc/paper/2021/file/bcb41ccdc4363c6848a1d760f26c28a0-Paper.pdf)\n- [NeurIPS 2021] **Model-based Domain Generalization** [[paper]](https://proceedings.neurips.cc/paper/2021/file/a8f12d9486cbcc2fe0cfc5352011ad35-Paper.pdf)\n- [ICCV 2021] **Selfreg: Self-Supervised Contrastive Regularization for Domain Generalization** [[paper]](http://openaccess.thecvf.com/content/ICCV2021/papers/Kim_SelfReg_Self-Supervised_Contrastive_Regularization_for_Domain_Generalization_ICCV_2021_paper.pdf)\n- [ICCV 2021] **A Simple Feature Augmentation for Domain Generalization** [[paper]](http://openaccess.thecvf.com/content/ICCV2021/papers/Li_A_Simple_Feature_Augmentation_for_Domain_Generalization_ICCV_2021_paper.pdf)\n- [ICLR 2022] **Gradient Matching for Domain Generalization** [[paper]](https://arxiv.org/pdf/2104.09937)\n- [CVPR 2022] **PCL: Proxy-Based Contrastive Learning for Domain Generalization** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Yao_PCL_Proxy-Based_Contrastive_Learning_for_Domain_Generalization_CVPR_2022_paper.pdf)\n- [ECCV 2022] **Domain Generalization By Mutual-Information Regularization With Pre-Trained Models** [[paper]](https://arxiv.org/pdf/2203.10789)\n- [ICML 2022] **Dna: Domain Generalization With Diversified Neural Averaging** [[paper]](https://proceedings.mlr.press/v162/chu22a/chu22a.pdf)\n- [NeurIPS 2022] **Domain Generalization Without Excess Empirical Risk** [[paper]](https://openreview.net/pdf?id=pluyPFTiTeJ)\n- [ICCV 2023] **PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization** [[paper]](https://arxiv.org/abs/2307.15199)\n\n**W/o domain label**\n\nExplicitly introduce compound domain generalization without domain labels (some previous work also don't rely on domain labels)\n\n- [CVPR 2022] **Compound Domain Generalization Via Meta-Knowledge Encoding** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Compound_Domain_Generalization_via_Meta-Knowledge_Encoding_CVPR_2022_paper.pdf)\n\n**Unsupervised**\n\n- [CVPR 2022] **Towards Unsupervised Domain Generalization** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Towards_Unsupervised_Domain_Generalization_CVPR_2022_paper.pdf)\n- [CVPR 2022] **Unsupervised Domain Generalization By Learning a Bridge Across Domains** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Harary_Unsupervised_Domain_Generalization_by_Learning_a_Bridge_Across_Domains_CVPR_2022_paper.pdf)\n\n### Domain Adaptation\n\nIn the setting of domain adaptation, the model can access partial target domain data (usually unsupervised). There are also many variants like open-set, evolving, dynamic domain adaptation, etc.\n\n- [ICML 2015] **Unsupervised Domain Adaptation By Backpropagation** [[paper]](http://proceedings.mlr.press/v37/ganin15.pdf)\n- [JMLR 2016] **Domain-Adversarial Training of Neural Networks** [[paper]](https://www.jmlr.org/papers/volume17/15-239/15-239.pdf)\n- [ECCV 2016] **Deep Coral: Correlation Alignment for Deep Domain Adaptation** [[paper]](https://arxiv.org/abs/1607.01719)\n- [NeurIPS 2016] **Unsupervised Domain Adaptation With Residual Transfer Networks** [[paper]](https://proceedings.neurips.cc/paper/2016/file/ac627ab1ccbdb62ec96e702f07f6425b-Paper.pdf)\n- [NeurIPS 2016] **Learning Transferrable Representations for Unsupervised Domain Adaptation** [[paper]](https://proceedings.neurips.cc/paper/2016/file/b59c67bf196a4758191e42f76670ceba-Paper.pdf)\n- [CVPR 2017] **Adversarial Discriminative Domain Adaptation** [[paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Tzeng_Adversarial_Discriminative_Domain_CVPR_2017_paper.pdf)\n- [ICDM 2017] **Balanced Distribution Adaptation for Transfer Learning** [[paper]](https://ieeexplore.ieee.org/iel7/8211002/8215462/08215613.pdf?casa_token=Kdomni1I_-YAAAAA:wg0xxHwxoJruSejuzmpfe66So7_WSL2Lr1yHa4OfdLVfhXSMxaqzDmTQwPEZkbmi2ggx_Ymqm4Nl)\n- [ECCV 2018] **Open set domain adaptation by backpropagation** [[paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper.pdf)\n- [AAAI 2018] **Multi-Adversarial Domain Adaptation** [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17067/16644)\n- [MM 2018] **Visual Domain Adaptation With Manifold Embedded Distribution Alignment** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3240508.3240512)\n- [CVPR 2019] **Universal Domain Adaptation** [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/You_Universal_Domain_Adaptation_CVPR_2019_paper.pdf)\n- [CVPR 2019] **Contrastive Adaptation Network for Unsupervised Domain Adaptation** [[paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Kang_Contrastive_Adaptation_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf)\n- [ICML 2019] **On Learning Invariant Representations for Domain Adaptation** [[paper]](http://proceedings.mlr.press/v97/zhao19a/zhao19a.pdf)\n- [CVPR 2020] **Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation** [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Reliable_Weighted_Optimal_Transport_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.pdf)\n- [ICML 2020] **Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation** [[paper]](http://proceedings.mlr.press/v119/liang20a/liang20a.pdf)\n- [TPAMI 2020] **Maximum Density Divergence for Domain Adaptation** [[paper]](https://ieeexplore.ieee.org/iel7/34/4359286/09080115.pdf?casa_token=DkacOMonv3gAAAAA:4DJ1THgujrfZcz3ITdd09Xo_RwkIp6RKS3-Fu2-ySg_I7xeIAFKeGL_GVl6zchVZTS2xq8LasuSk)\n- [AAAI 2020] **Adversarial Domain Adaptation with Domain Mixup** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/6123)\n- [WACV 2021] **Dacs: Domain Adaptation Via Cross-Domain Mixed Sampling** [[paper]](https://openaccess.thecvf.com/content/WACV2021/papers/Tranheden_DACS_Domain_Adaptation_via_Cross-Domain_Mixed_Sampling_WACV_2021_paper.pdf)\n- [CVPR 2021] **Dynamic Weighted Learning for Unsupervised Domain Adaptation** [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_Dynamic_Weighted_Learning_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf)\n- [ICML 2022] **Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation** [[paper]](https://proceedings.mlr.press/v162/shen22d/shen22d.pdf)\n- [TPAMI 2022] **Cross-domain Contrastive Learning for Unsupervised Domain Adaptation** [[paper]](https://ieeexplore.ieee.org/iel7/6046/4456689/09695359.pdf?casa_token=BQ17etjoVUwAAAAA:pSLouzFWW4fQm_klgNughmr8NxIojk1tPFvmV5p271abcjn9MSzC_6p_i6JAWX0S3rEy9fIF5_iK)\n- [WACV 2022] **Federated Multi-target Domain Adaptation** [[paper]](https://openaccess.thecvf.com/content/WACV2022/papers/Yao_Federated_Multi-Target_Domain_Adaptation_WACV_2022_paper.pdf)\n\n### Test-time Adaptation/Training\n\nIn the scenario of test-time adaptation, the model is pre-trained on the source domain data and is adapted during the testing on the target domain.\n\n- [ICML 2020] **Test-time training with self-supervision for generalization under distribution shifts** [[paper]](http://proceedings.mlr.press/v119/sun20b/sun20b.pdf)\n- [MICCAI 2020] **Test-time Unsupervised Domain Adaptation** [[paper]](https://arxiv.org/pdf/2010.01926)\n- [ICLR 2021] **Tent: Fully test-time adaptation by entropy minimization** [[paper]](https://arxiv.org/pdf/2006.10726)\n- [NeurIPS 2021] **TTT++: When Does Self-supervised Test-time Training Fail or Thrive?** [[paper]](https://proceedings.neurips.cc/paper/2021/file/b618c3210e934362ac261db280128c22-Paper.pdf)\n- [ICML 2022] **Mt3: Meta Test-time Training for Self-supervised Test-time Adaption** [[paper]](https://proceedings.mlr.press/v151/bartler22a/bartler22a.pdf)\n- [CVPR 2022] **Contrastive Test-Time Adaptation** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Contrastive_Test-Time_Adaptation_CVPR_2022_paper.pdf)\n- [CVPR 2022] **Parameter-free Online Test-time Adaptation** [[paper]](http://openaccess.thecvf.com/content/CVPR2022/papers/Boudiaf_Parameter-Free_Online_Test-Time_Adaptation_CVPR_2022_paper.pdf)\n- [NeurIPS 2022] **Test-Time Adaptation via Conjugate Pseudo-labels** [[paper]](https://arxiv.org/pdf/2207.09640)\n- [ICML 2022] **Efficient Test-Time Model Adaptation without Forgetting** [[paper]](https://arxiv.org/pdf/2204.02610)\n- [NeurIPS 2022] **Test-time Training with Masked Autoencoders** [[paper]](https://arxiv.org/pdf/2209.07522)\n- [NeurIPS 2022] **Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts** [[paper]](https://arxiv.org/pdf/2210.03885)\n\nAlso known as source-free domain adaptation\n- [CVPR 2020] **Universal Source-Free Domain Adaptation** [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.pdf)\n- [CVPR 2020] **Model adaptation: Unsupervised domain adaptation without source data** [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Model_Adaptation_Unsupervised_Domain_Adaptation_Without_Source_Data_CVPR_2020_paper.pdf)\n- [ICCV 2021] **Generalized Source-Free Domain Adaptation** [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Generalized_Source-Free_Domain_Adaptation_ICCV_2021_paper.pdf)\n- [ICCV 2021] **Adaptive Adversarial Network for Source-Free Domain Adaptation** [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Xia_Adaptive_Adversarial_Network_for_Source-Free_Domain_Adaptation_ICCV_2021_paper.pdf)\n- [CVPR 2022] **Source-Free Domain Adaptation via Distribution Estimation** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Source-Free_Domain_Adaptation_via_Distribution_Estimation_CVPR_2022_paper.pdf)\n- [ICML 2022] **Balancing Discriminability and Transferability for Source-Free Domain Adaptation** [[paper]](https://arxiv.org/abs/2206.08009?context=cs.LG)\n- [NeurIPS 2022] **Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation** [[paper]](https://openreview.net/forum?id=ZlCpRiZN7n)\n- [NeurIPS 2022] **Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning** [[paper]](https://openreview.net/forum?id=NjImFaBEHl)\n## Data Modality\n\nAbove papers study the distribution shift on images. There are also many applications to other data modalities.\n\n### Graph\nAdaptation\n- [TKDE 2022] **Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution** [[paper (2019)]](https://arxiv.org/pdf/1909.01541)\n- [IJCAI 2019] **DANE: Domain Adaptive Network Embedding** [[paper]](https://arxiv.org/pdf/1906.00684)\n- [WWW 2020] **Unsupervised Domain Adaptive Graph Convolutional Networks** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3366423.3380219?casa_token=UVont2gPk00AAAAA:jWxayKpfsmAPqYQspH9R-s_b5wpEcNRoE-ViV6Cwznxo2HiRtVSykmq5RHHXn1_MZYuiVOWTdS_OzS0)\n- [ICLR 2022] **Graph-Relational Domain Adaptation** [[paper]](https://arxiv.org/pdf/2202.03628.pdf)\n- [AAAI 2023] **Non-IID Transfer Learning on Graphs** [[paper]](https://arxiv.org/pdf/2212.08174.pdf)\n- [ICLR 2023] **Graph Domain Adaptation via Theory-Grounded Spectral Regularization** [[paper]](https://openreview.net/forum?id=OysfLgrk8mk)\n\nGeneralization\n- [ICLR 2022] **Handling Distribution Shifts on Graphs: An Invariance Perspective** [[paper]](https://arxiv.org/pdf/2202.02466)\n- [ICLR 2022] **Discovering Invariant Rationales for Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2201.12872)\n- [ICML 2022] **Interpretable and Generalizable Graph Learning Via Stochastic Attention Mechanism** [[paper]](https://proceedings.mlr.press/v162/miao22a/miao22a.pdf)\n- [KDD 2022] **Causal Attention for Interpretable and Generalizable Graph Classification** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539366?casa_token=zKvdUqGb124AAAAA:Rg8bhY1lJeqahX1PHEVUs7UbtWDjuihnvfbEfOvTgBt6MO1rAF5y3L7sCj0CbJTMN8dLvGFkFzWPK_w)\n- [NeurIPS 2022] **Dynamic Graph Neural Networks Under Spatio-temporal Distribution Shift** [[paper]](https://openreview.net/pdf?id=1tIUqrUuJxx)\n- [NeurIPS 2022] **Learning Substructure Invariance for Out-of-Distribution Molecular Representations** [[paper]](https://openreview.net/pdf?id=2nWUNTnFijm)\n- [NeurIPS 2022] **Learning Invariant Graph Representations for Out-of-Distribution Generalization** [[paper]](https://openreview.net/pdf?id=acKK8MQe2xc)\n- [NeurIPS 2022] **Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs** [[paper]](https://openreview.net/pdf?id=A6AFK_JwrIW)\n- [WSDM 2023] **Alleviating Structural Distribution Shift in Graph Anomaly Detection** [[paper]](http://staff.ustc.edu.cn/~hexn/papers/wsdm23-GDN.pdf)\n\nTest-time adaptation\n- [arxiv 2022] **Test-Time Training for Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2210.08813)\n- [arxiv 2022] **Empowering Graph Representation Learning with Test-time Graph Transformation** [[paper]](https://arxiv.org/pdf/2210.03561)\n\n### Text\nMany applications in different NLP tasks.\n- [CVPR 2019] **Sequence-to-sequence Domain Adaptation Network for Robust Text Image Recognition** [[paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Sequence-To-Sequence_Domain_Adaptation_Network_for_Robust_Text_Image_Recognition_CVPR_2019_paper.pdf)\n- [NAACL 2019] **Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation** [[paper]](https://aclanthology.org/N19-1209/)\n- [AAAI 2020] **Multi-source Domain Adaptation for Text Classification Via Distancenet-bandits** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/download/6288/6144)\n- [COLING 2020] **Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations** [[paper]](https://aclanthology.org/2020.coling-main.338/)\n- [ACL 2020] **Pretrained Transformers Improve Out-of-distribution Robustness** [[paper]](https://arxiv.org/pdf/2004.06100)\n- [EMNLP 2021] **Contrastive Domain Adaptation for Question Answering Using Limited Text Corpora** [[paper]](https://arxiv.org/pdf/2108.13854)\n- [EMNLP 2021] **Pdaln: Progressive Domain Adaptation over a Pre-trained Model for Low-resource Cross-domain Named Entity Recognition** [[paper]](https://aclanthology.org/2021.emnlp-main.442/)\n- [ACL 2021] **Matching Distributions Between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation** [[paper]](https://aclanthology.org/2021.acl-long.421/)\n- [ACL 2021] **Bridge-based Active Domain Adaptation for Aspect Term Extraction** [[paper]](https://aclanthology.org/2021.acl-long.27/)\n- [ECCV 2022] **Grounding Visual Representations with Texts for Domain Generalization** [[paper]](https://arxiv.org/pdf/2207.10285)\n- [KAIS 2022] **Knowledge distillation for bert unsupervised domain adaptation** [[paper]](https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1007/s10115-022-01736-y\u0026casa_token=VgVxkeLR5wcAAAAA:ujMylYAlDW-elyTA6SHGl2L0zl_M32WSZIiwLSSwKfsBnUrJ6aaLJRhLx9U-lD1Wxc_3ty2tZzwsp-fe7jc)\n- [ACL 2022] **Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network** [[paper]](https://aclanthology.org/2022.acl-long.74/)\n...\n\n\n### Time Series\n- [ICLR 2017] **Variational Recurrent Adversarial Deep Domain Adaptation** [[paper]](https://openreview.net/pdf?id=rk9eAFcxg)\n- [KDD 2020] **Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data** [[paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403228)\n- [AAAI 2021] **Time Series Domain Adaptation Via Sparse Associative Structure Alignment** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16846/16653)\n- [IJCAI 2021] **Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation** [[paper]](http://palm.seu.edu.cn/hxue/papers/Adversarial%20spectral%20kernel%20matching%20for%20unsupervised%20time%20series%20domain%20adaption.pdf)\n- [CHIL 2021] **An Empirical Framework for Domain Generalization in Clinical Settings** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3450439.3451878)\n- [TNNLS 2022] **Self-Supervised Autoregressive Domain Adaptation for Time Series Data** [[paper]](https://ieeexplore.ieee.org/iel7/5962385/6104215/09804766.pdf?casa_token=RTkDlGkCGEAAAAAA:4Fom6O1eqOMzJziD-1dN1N29GtUmj_BI3OIYAP_sVfrEAMF0bRsbFlVLHjTR_EAmPaYrX5vIC2sj)\n- [MM 2022] **Domain Adaptation for Time-series Classification to Mitigate Covariate Shift** [[paper]](https://dl.acm.org/doi/abs/10.1145/3503161.3548167)\n- [ICRA 2022] **Causal-based Time Series Domain Generalization for Vehicle Intention Prediction** [[paper]](https://ieeexplore.ieee.org/iel7/9811522/9811357/09812188.pdf?casa_token=e2xDhMqO680AAAAA:RuW4P1FXIX69pNB5LGuerK2kjPB4EMvf8eyg13aW5kWcx8-JhsVpo-aRj5UUFShDcfTQpbi0Mpw6)\n- [ICML 2022] **Domain Adaptation for Time Series Forecasting via Attention Sharing** [[paper]](https://proceedings.mlr.press/v162/jin22d/jin22d.pdf)\n\n### Video\n- [ICCV 2019] **Temporal Attentive Alignment for Large-scale Video Domain Adaptation** [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Temporal_Attentive_Alignment_for_Large-Scale_Video_Domain_Adaptation_ICCV_2019_paper.pdf)\n- [ECCV 2020] **Shuffle and Attend: Video Domain Adaptation** [[paper]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570664.pdf)\n- [ICCV 2021] **Learning Cross-modal Contrastive Features for Video Domain Adaptation** [[paper]](http://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Learning_Cross-Modal_Contrastive_Features_for_Video_Domain_Adaptation_ICCV_2021_paper.pdf)\n- [NeurIPS 2021] **Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing** [[paper]](https://proceedings.neurips.cc/paper/2021/file/c47e93742387750baba2e238558fa12d-Paper.pdf)\n- [ECCV 2022] **Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition** [[paper]](https://openreview.net/pdf?id=Jd2WAZomD8r)\n- [WACV 2022] **Domain Generalization Through Audio-visual Relative Norm Alignment in First Person Action Recognition** [[paper]](https://openaccess.thecvf.com/content/WACV2022/papers/Planamente_Domain_Generalization_Through_Audio-Visual_Relative_Norm_Alignment_in_First_Person_WACV_2022_paper.pdf)\n\n### Speech\n- [SPL 2014] **Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition** [[paper]](https://ieeexplore.ieee.org/iel7/97/4358004/06817520.pdf?casa_token=BtB4htXN5ZEAAAAA:NIbwEWYQN1pY2zL8WuLt5YMOPL4P9TJes_LlrOr3QeQgAG_wpdoSj3HYmon80YFQ81L7dAy2BE77)\n- [ICASSP 2015] **Supervised Domain Adaptation for Emotion Recognition from Speech** [[paper]](https://ieeexplore.ieee.org/iel7/7158221/7177909/07178934.pdf?casa_token=f2ORpk1V0HUAAAAA:NnEYZ7LUMuyGM4NCT6v1ZqE8_ngQfhrhCyl3Ca9JT5W0OpK-5sdtyofX_Hxf0bgSrBeQLSoFi7Mz)\n- [NeuroComputing 2017] **An Unsupervised Deep Domain Adaptation Approach for Robust Speech Recognition** [[paper]](https://www.sciencedirect.com/science/article/pii/S0925231217301492?casa_token=UU2tzU41VloAAAAA:F4mnzwMpjSqPZIyeY9Pn-gf1PwbcavrqXorANTc4ROiFmhpnBophRyzKDxvvnS3y3v0O0h-Xnl8g)\n- [ASRU 2019] **Domain Adaptation Via Teacher-student Learning for End-to-end Speech Recognition** [[paper]](https://ieeexplore.ieee.org/iel7/8985378/9003727/09003776.pdf?casa_token=0tOiorcsjfwAAAAA:oHUJVPvDvuamvIhbczSXuc-ioli3SsVldPfv36noXDGnMCRVJNEGiY5lJoorYNvP2DrM9J-TsaWE)\n- [TAC 2021] **Improving Cross-corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (addog)** [[paper]](https://ieeexplore.ieee.org/iel7/5165369/5520654/08713918.pdf?casa_token=MBUOb9dzmJwAAAAA:YX-po4wbOviRVdYCR412Y6UbI4xgx1ff28dIGlb2qgQEDcTdnkZRQYmOZkOBEDAjphLlBUCUcKoE)\n- [SLT 2021] **Domain Generalization with Triplet Network for Cross-corpus Speech Emotion Recognition** [[paper]](https://ieeexplore.ieee.org/iel7/9383468/9383452/09383534.pdf?casa_token=5mLtgBnS4G4AAAAA:GstUzDn2mKQylWq44KNZ7EAaeE-caU8h34jNqukejKThNrVaAgo8ndHgVt3P3HC2uFj51R9dXbfW)\n- [ICASSP 2022] **Large-scale Asr Domain Adaptation Using Self-and Semi-supervised Learning** [[paper]](https://ieeexplore.ieee.org/iel7/9745891/9746004/09746719.pdf?casa_token=aKezGIHnTOsAAAAA:WqAJ11D0UFsPX-hJrZIKpOXUB-rqExnBfT9zeuyZuc8LUx-X4-SRnf3U7FV2yP6EuxLhgvsFp7z0)\n\n\n### Tabular Data\n- [NeurIPS 2022] **Distribution-Informed Neural Networks for Domain Adaptation Regression** [[paper]](https://openreview.net/pdf?id=8hoDLRLtl9h)\n- [NeurIPS 2022] **C-Mixup: Improving Generalization in Regression** [[paper]](https://arxiv.org/pdf/2210.05775)\n\n### Others (RecSys)\n- [KDD 2021] **Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System** [[paper]](https://arxiv.org/pdf/2010.15363)\n- [WWW 2021] **Disentangling User Interest and Conformity for Recommendation with Causal Embedding** [[paper]](https://hexiangnan.github.io/papers/www21-dice.pdf)\n- [SIGIR 2021] **Causal Intervention for Leveraging Popularity Bias in Recommendation** [[paper]](https://arxiv.org/pdf/2105.06067)\n- [SIGIR 2021] **Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue** [[paper]](https://arxiv.org/pdf/2009.09945)\n- [SIGIR 2021] **AutoDebias: Learning to Debias for Recommendation** [[paper]](https://hexiangnan.github.io/papers/sigir21-AutoDebias.pdf)\n- [WWW 2022] **Cross Pairwise Ranking for Unbiased Item Recommendation** [[paper]](https://hexiangnan.github.io/papers/www22-cpr.pdf)\n- [WWW 2022] **Causal Representation Learning for Out-of-Distribution Recommendation** [[paper]](https://hexiangnan.github.io/papers/www22-ood-rec.pdf)\n- [SIGIR 2022] **Interpolative Distillation for Unifying Biased and Debiased Recommendation** [[paper]](https://hexiangnan.github.io/papers/sigir22-InterD.pdf)\n- [TOIS 2022] **Addressing Confounding Feature Issue for Causal Recommendation** [[paper]](https://arxiv.org/pdf/2205.06532.pdf)\n- [TKDE 2023] **Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation** [[paper]](https://hexiangnan.github.io/papers/tkde23-TIDE.pdf)\n\n...\n\n## Decentralized (Federated)\nBesides generalization of centralized learning, transferability of decentralized setting (Federated Learning) has also received attention.\n- [arxiv 2018] **Federated Learning with Non-IID Data** [[paper]](https://arxiv.org/pdf/1806.00582)\n- [ICML 2019] **Agnostic Federated Learning** [[paper]](http://proceedings.mlr.press/v97/mohri19a/mohri19a.pdf)\n- [MLSys 2020] **Federated Optimization in Heterogeneous Networks** [[paper]](https://arxiv.org/pdf/1812.06127)\n- [ICML 2020] **SCAFFOLD: Stochastic Controlled Averaging for Federated Learning** [[paper]](https://arxiv.org/pdf/1910.06378)\n- [NeurIPS 2020] **Robust Federated Learning: the Case of Affine Distribution Shifts** [[paper]](https://proceedings.neurips.cc/paper/2020/file/f5e536083a438cec5b64a4954abc17f1-Paper.pdf)\n- [ICLR 2021] **Fedbn: Federated Learning on Non-iid Features Via Local Batch Normalization** [[paper](https://arxiv.org/pdf/2102.07623)\n- [SDM 2021] **Fairness-aware Agnostic Federated Learning** [[paper]](https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.21)\n- [CVPR 2021] **FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space** [[paper]](https://arxiv.org/pdf/2103.06030)\n- [NeurIPS 2022] **FedSR: A Simple and Effective Domain Generalization Method for Federated Learning** [[paper]](https://openreview.net/pdf?id=mrt90D00aQX)\n- [TPDS 2022] **Flexible Clustered Federated Learning for Client-level Data Distribution Shift** [[paper]](https://ieeexplore.ieee.org/iel7/71/4359390/09647969.pdf?casa_token=ppLa63KqI0kAAAAA:9tkLoz7O69FS2uFwzU80K8PHdMxEk7CPTBg5YRHs6JigACFKJJuNRvupZ1U8-a3GrBom2KMBEQ)\n- [CVPR 2022] **Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.pdf)\n- [CVPR 2022] **Feddc: Federated Learning with Non-iid Data Via Local Drift Decoupling and Correction** [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.pdf)\n\n## Detection\nBesides generalization, other perspectives including detection, fairness robustness, etc. are also studied.\n\nOut-of-distribution detection, outlier detection and anomaly detection.\n- [ICDM 2018] **Adversarially Learned Anomaly Detection** [[paper]](https://ieeexplore.ieee.org/iel7/8591042/8594809/08594897.pdf?casa_token=BpGIhSrJhSsAAAAA:HsmEM6bEK8vWAobZa-XzGhRCzwQV5-y4SsaDQS5er2TxRewJXhS8FltoEJOiujoT3mM4O5DGnQ)\n- [arxiv 2018] **Learning Confidence for Out-of-distribution Detection in Neural Networks** [[paper]](https://arxiv.org/pdf/1802.04865)\n- [NeurIPS 2019] **Likelihood Ratios for Out-of-distribution Detection** [[paper]](https://proceedings.neurips.cc/paper/2019/file/1e79596878b2320cac26dd792a6c51c9-Paper.pdf)\n- [IJCNN 2019] **XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning** [[paper]](https://arxiv.org/pdf/1912.00290)\n- [ICDM 2020] **Copod: Copula-based Outlier Detection** [[paper]](https://ieeexplore.ieee.org/iel7/9338245/9338248/09338429.pdf?casa_token=KJ7C5SHVJnAAAAAA:HSpMqKX4ovkFnSM7E286Ri4XvgkPo_knawVVDRzDuGLLYsoBAsLkaKlZH_AAjlSzzLHO9Q_A8w)\n- [NeurIPS 2020] **Energy-based Out-of-distribution Detection** [[paper]](https://proceedings.neurips.cc/paper/2020/file/f5496252609c43eb8a3d147ab9b9c006-Paper.pdf)\n- [CVPR 2020] **Learning Memory-guided Normality for Anomaly Detection** [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Park_Learning_Memory-Guided_Normality_for_Anomaly_Detection_CVPR_2020_paper.pdf)\n- [NeurIPS 2021] **Exploring the Limits of Out-of-distribution Detection** [[paper]](https://proceedings.neurips.cc/paper/2021/file/3941c4358616274ac2436eacf67fae05-Paper.pdf)\n- [NeurIPS 2021] **Automatic Unsupervised Outlier Model Selection** [[paper]](https://proceedings.neurips.cc/paper/2021/file/23c894276a2c5a16470e6a31f4618d73-Paper.pdf)\n- [ICCV 2021] **Divide-and-assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection** [[paper]](http://openaccess.thecvf.com/content/ICCV2021/papers/Hou_Divide-and-Assemble_Learning_Block-Wise_Memory_for_Unsupervised_Anomaly_Detection_ICCV_2021_paper.pdf)\n- [CVPR 2021] **Anomaly Detection in Video Via Self-supervised and Multi-task Learning** [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.pdf)\n- [AAAI 2022] **On the Impact of Spurious Correlation for Out-of-distribution Detection** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/21244/20993)\n- [AAAI 2022] **Lunar: Unifying Local Outlier Detection Methods Via Graph Neural Networks** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/20629/20388)\n- [ICML 2022] **Poem: Out-of-distribution Detection with Posterior Sampling** [[paper]](https://proceedings.mlr.press/v162/ming22a/ming22a.pdf)\n- [ECCV 2022] **Dice: Leveraging Sparsification for Out-of-distribution Detection** [[paper]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136840680.pdf)\n\n## Fairness\n- [FAccT 2021] **Fairness Violations and Mitigation Under Covariate Shift** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3442188.3445865)\n- [AAAI 2021] **Robust Fairness Under Covariate Shift** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17135/16942)\n- [NeurIPS 2022] **Diagnosing Failures of Fairness Transfer Across Distribution Shift in Real-world Medical Settings** [[paper]](https://openreview.net/pdf?id=K-A4tDJ6HHf)\n- [NeurIPS 2022] **Fairness Transferability Subject to Bounded Distribution Shift** [[paper]](https://arxiv.org/pdf/2206.00129)\n- [NeurIPS 2022] **Transferring Fairness under Distribution Shifts via Fair Consistency Regularization** [[paper]](https://arxiv.org/pdf/2206.12796)\n- [arxiv 2022] **How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts** [[paper]](https://arxiv.org/pdf/2207.01168)\n\n## Robustness\n- [ICLR 2019] **On the Sensitivity of Adversarial Robustness to Input Data Distributions** [[paper]](https://arxiv.org/pdf/1902.08336)\n- [CVPR 2021] **Adversarial Robustness under Long-Tailed Distribution** [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Adversarial_Robustness_Under_Long-Tailed_Distribution_CVPR_2021_paper.pdf)\n- [arxiv 2022] **BOBA: Byzantine-Robust Federated Learning with Label Skewness** [[paper]](https://arxiv.org/pdf/2208.12932)\n- [arxiv 2022] **Generalizability of Adversarial Robustness Under Distribution Shifts** [[paper]](https://arxiv.org/pdf/2209.15042)\n\n\n## Learning Strategy\n- [ICLR 2020] **Learning to Balance: Bayesian Meta-learning for Imbalanced and Out-of-distribution Tasks** [[paper]](https://arxiv.org/pdf/1902.08336)\n- [NeurIPS 2020] **OOD-MAML: Meta-learning for few-shot out-of-distribution detection and classification** [[paper]](https://proceedings.neurips.cc/paper/2020/file/28e209b61a52482a0ae1cb9f5959c792-Paper.pdf)\n- [NeurIPS 2020] **Task-robust Model-agnostic Meta-learning** [[paper]](https://proceedings.neurips.cc/paper/2020/file/da8ce53cf0240070ce6c69c48cd588ee-Paper.pdf)\n- [TIP 2021] **Domain Adaptive Ensemble Learning** [[paper]](https://ieeexplore.ieee.org/iel7/83/4358840/09540778.pdf?casa_token=tHapCRZf2aEAAAAA:q2BwltKkK2kZT37g1yjmHTG70IYrcPay4RMq0m7jMWrqFntuMuRnSxYGYYytxXyLIIc9NjiyuQ)\n- [NeurIPS 2021] **Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution** [[paper]](https://proceedings.neurips.cc/paper/2021/file/1e932f24dc0aa4e7a6ac2beec387416d-Paper.pdf)\n- [ICLR 2022] **Deep Ensembling with No Overhead for either Training or Testing: The All-Round B- lessings of Dynamic Sparsity** [[paper]](https://arxiv.org/pdf/2106.14568)\n- [NeurIPS 2022] **Improving Multi-Task Generalization via Regularizing Spurious Correlation** [[paper]](https://arxiv.org/pdf/2205.09797.pdf)\n\n...\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/weitianxin%2Fawesome-distribution-shift/projects"}