domain_adapt_ood_bias.md

Domain Adaptation, Generalization, Out-of-Distribution and Debiasing

Domain Adaptation

• [2014 ICML] Heterogeneous Domain Adaptation for Multiple Classes, [paper], [bibtex].
• [2015 ICML] Unsupervised Domain Adaptation by Backpropagation, [paper], [bibtex], sources: [shucunt/domain_adaptation], [pumpikano/tf-dann], [kskdev/DANN], [fungtion/DANN].
• [2016 NIPS] Domain Separation Networks, [paper], [bibtex], sources: [tensorflow/models/research/domain_adaptation], [fungtion/DSN], [wj926/DomainSeparationNetworks].
• [2016 JMLR] Domain-Adversarial Training of Neural Networks, [paper], [bibtex], sources: [shucunt/domain_adaptation], [pumpikano/tf-dann], [kskdev/DANN], [fungtion/DANN].
• [2018 NIPS] Conditional Adversarial Domain Adaptation, [paper], [bibtex], sources: [thuml/CDAN].
• [2019 JMLR] Multi-class Heterogeneous Domain Adaptation, [paper], [bibtex].
• [2019 ICML] On Learning Invariant Representations for Domain Adaptation, [paper], [bibtex].
• [2020 AAAI] Unsupervised Domain Adaptation on Reading Comprehension, [paper], [bibtex], sources: [caoyu1991/CASe].
• [2020 ArXiv] Tackling Unsupervised Multi-source Domain Adaptation with Optimism and Consistency, [paper], [bibtex], sources: [dpernes/modafm].
• [2021 ICML] Delving into Deep Imbalanced Regression, [paper], [bibtex], sources: [YyzHarry/imbalanced-regression].
• [2021 ICCV] Transporting Causal Mechanisms for Unsupervised Domain Adaptation, [paper], [bibtex], sources: [yue-zhongqi/tcm].
• [2021 NAACL] Domain Divergences: A Survey and Empirical Analysis, [paper], [bibtex].

Domain Generalization

• [2016 AAAI] Return of Frustratingly Easy Domain Adaptation, [paper], [bibtex].
• [2018 AAAI] Learning to Generalize: Meta-Learning for Domain Generalization, [paper], [bibtex], sources: [HAHA-DL/MLDG].
• [2019 ICML] Domain Agnostic Learning with Disentangled Representations, [paper], [bibtex], [supplementary], sources: [VisionLearningGroup/DAL].
• [2019 ICCV] Episodic Training for Domain Generalization, [paper], [bibtex], sources: [HAHA-DL/Episodic-DG].
• [2019 NeurIPS] Domain Generalization via Model-Agnostic Learning of Semantic Features, [paper], [bibtex], sources: [biomedia-mira/masf].

Out-of-Distribution Learning

• [2020 NeurIPS] Energy-based Out-of-distribution Detection, [paper], [bibtex], sources: [wetliu/energy_ood].
• [2020 ArXiv] Out-of-Distribution Generalization via Risk Extrapolation, [paper], [bibtex], sources: [capybaralet/REx_code_release].
• [2020 ArXiv] Unshuffling Data for Improved Generalization, [paper], [bibtex].
• [2020 ICLR] Your classifier is secretly an energy based model and you should treat it like one, [paper], [bibtex], sources: [wgrathwohl/JEM].
• [2021 ICML] Improved OOD Generalization via Adversarial Training and Pre-training, [paper], [bibtex], [supplementary].
• [2021 ArXiv] The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning, [paper], [bibtex].

Debiasing

• [2020 ICML] Learning De-biased Representations with Biased Representations, [paper], [bibtex], sources: [clovaai/rebias].
• [2020 NeurIPS] Learning from Failure: Training Debiased Classifier from Biased Classifier, [paper], [bibtex], sources: [alinlab/LfF].