Awesome-Domain-Generalization
Awesome things about domain generalization, including papers, code, etc.
https://github.com/junkunyuan/Awesome-Domain-Generalization
Last synced: 14 days ago
JSON representation
Applications
Face Recognition & Anti-Spoofing
- [CVPR 2019 - MADDoG)] (MADDG) [78]
- [CVPR 2020 - pei/SSDG-CVPR2020)] (SSDG) [79]
- [CVPR 2020
- [CVPR 2020
Person Re-Identification
- [TIP 2021 - AAE**) [144]
- [AAAI 2020
- [ICLR 2021 - release)] (MixStyle) [56]
- [CVPR 2021
- [CVPR 2021
- [CVPR 2021
- [NeurIPS 2021
- [TPAMI 2021 - person-reid)] [114]
Dataset
- [ICCV 2013
- [CVPR 2016
- [ECCV 2016
- [arXiv 2017 - 17 dataset**) [36]
- [CVPR 2017
- [ECCV 2018
- [ICLR 2022 - Liang/MetaShift)] (MetaShift dataset) [213]
- [ICLR 2019 - 10-C / CIFAR-100-C / ImageNet-C dataset**) [37]
- [PR 2021
- [arXiv 2022
- [arXiv 2017 - 17 dataset**) [36]
- [ICLR 2019 - 10-C / CIFAR-100-C / ImageNet-C dataset**) [37]
- [CVPR 2012 - Caltech dataset**) [32]
- [ICCV 2015
Domain Generalization
Causality-Based Methods
- [arXiv 2019
- [ICML 2021
- [NeurIPS 2021
- [ICCV 2021
- [arXiv 2022
- [ICML 2021
- [NeurIPS 2021 - IRM)] (IB-ERM, IB-IRM) [207]
- [CVPR 2021
- [NeurIPS 2021 - semantic-generative-model)] (CSG-ind) [145]
- [NeurIPS 2021
- [AAAI 2022
- [arXiv 2021 - DG**) [163]
- [TPAMI 2021
- [arXiv 2022 - CORAL**, CausIRL-MMD) [216]
- [KDD 2023 - Inference-via-Style-Transfer-for-OOD-Generalisation)] (FAST, FAFT, FAGT) [233]
- [CVPR 2021
Data Augmentation-Based Methods
- [arXiv 2017 - lab/certifiable-distributional-robustness)] [52]
- [ICLR 2018
- [NeurIPS 2018 - unseen-domains)] [25]
- [WACV 2019 - DG)] [167]
- [ICCV 2019 - shift-robustness)] [21]
- [ICCV 2019
- [ICCV workshop 2019
- [Frontiers in Cardiovascular Medicine 2020
- [TMI 2020
- [ICPR 2020
- [arXiv 2021 - release)] (MixStyle) [58]
- [ICLR 2021
- [CVPR 2021
- [ICCV 2021
- [TPAMI 2021
- [arXiv 2020
- [ICLR 2022
- [Frontiers in Bioengineering and Biotechnology 2019
- [Frontiers in Bioengineering and Biotechnology 2019
- [MM 2021
- [ICCV 2019
- [Frontiers in Bioengineering and Biotechnology 2019
- [Frontiers in Cardiovascular Medicine 2020
- [CVPR 2021
- [CVPR 2020
- [CVPR 2021 - ELCFS)] (FedDG) [147]
- [CVPR 2021
- [CVPR 2021 - DR**) [153]
- [CVPR 2021 - DAML)] (DAML) [119]
Disentangled Representation Learning-Based Methods
- [ECCV 2012 - bias)] [103]
- [ICML workshop 2019 - Amsterdam/DIVA)] (DIVA) [107]
- [ICML 2020
- [ECCV 2020
- [CVPR 2021
- [CVPR 2021
- [ICCV 2021
- [ICCV 2021
- [TMLR 2022
- [AAAI 2021
- [arXiv 2021
- [ICIP 2021
- [ICML workshop 2019 - Amsterdam/DIVA)] (DIVA) [107]
- [ICML 2020
- [MM 2021
- [WACV 2023
- [CVPR 2020
Domain Alignment-Based Methods
- [ICML 2013 - dica)] (DICA) [65]
- [JMLR 2016
- [CVPR 2016
- [IJCAI 2016
- [TPAMI 2017
- [ICCV 2017
- [arXiv 2018
- [AAAI 2018
- [CVPR 2018 - AAE**) [76]
- [ECCV 2018
- [arXiv 2019
- [MICCAI 2019
- [ECMLPKDD 2019
- [arXiv 2020
- [PR 2020 - DA-DG)] (CAADA) [80]
- [AAAI 2020 - tokyo/dg_mmld)] [83]
- [ISBI 2020
- [ICPR 2020
- [UAI 2020 - Discriminant-Analysis)] (MDA) [70]
- [NeurIPS 2020
- [NeurIPS 2020 - zhao/DG_via_ER)] [86]
- [ICCV 2021
- [arXiv 2019
- [arXiv 2020
- [arXiv 2021
- [IJCAI 2021
- [TMLR 2022
- [ICML 2013 - dica)] (DICA) [65]
- [AAAI 2018
- [UAI 2020 - Discriminant-Analysis)] (MDA) [70]
- [TIP 2021 - AAE**) [144]
- [ICML 2021
- [ECCV 2022
- [WACV 2024 - distribution-matching)] (RDM) [234]
- [ECCV 2018
- [CVPR 2019 - MADDoG)] (MADDG) [78]
- [CVPR 2020 - pei/SSDG-CVPR2020)] (SSDG) [79]
- [CVPR 2021 - Polaris/InferenceTimeDG)] [118]
- [CVPR 2021
Ensemble Learning-Based Methods
- [ECCV 2014
- [TNNLS 2018
- [TIP 2017
- [IEEE Robotics and Automation Letters 2018
- [GCPR 2018 - SAMs**) [92]
- [ICIP 2018
- [TMI 2020
- [TMI 2020 - Net)] (MS-Net) [95]
- [arXiv 2021
- [arXiv 2021
- [ICLR workshop 2021
- [MM 2021
- [ICCV 2015
Inference-Time-Based Methods
- [CVPR 2021 - Polaris/InferenceTimeDG)] [118]
- [CVPR 2021 - ERM**) [132]
- [NeurIPS 2021
- [CVPR 2021 - ERM**) [132]
Information-Based Methods
Meta-Learning-Based Methods
- [NeurIPS 2019 - mira/masf)] (MASF) [18]
- [arXiv 2020 - RIPL/DomainGeneralization-Stylization)] [60]
- [TNNLS 2020
- [MICCAI 2020
- [arXiv 2021
- [ICIP 2021 - EML**) [180]
- [ICML 2019 - Critic**) [5]
- [TNNLS 2020
Neural Architecture Search-based Methods
- [ICCV 2021 - OoD**) [129]
Normalization-Based Methods
- [ICLR 2021 - AI/MetaNorm)] (MetaNorm) [19]
- [arXiv 2020
- [ECCV 2020
- [WACV 2022 - Net**) [186]
- [ECCV 2016
Regularization-Based Methods
- [CVPR 2021 - SJTU/FACT)] (FACT) [160]
- [NeurIPS 2021
- [NeurIPS 2018
- [ICCV 2021
- [NeurIPS 2011
- [ECCV 2020
- [NeurIPS 2020 - OOD)] [181]
- [arXiv 2021
- [ICCV 2021
- [NeurIPS 2021
- [NeurIPS 2021 - for-the-Future)] (GI) [204]
- [NeurIPS 2021
- [NeurIPS 2021
- [ICLR 2021
- [ICLR 2020
- [arXiv 2021
- [arXiv 2021
- [ICLR 2022
- [ICML 2022
- [ICML 2021
- [AISTATS 2023
Self-Supervised Learning-Based Methods
- [CVPR 2019
- [ECCV 2020 - sjwang/EISNet)] (EISNet) [99]
- [NeurIPS 2021
- [ICLR 2020
- [arXiv 2020
- [BMVC 2020
- [TPAMI 2021 - Supervised_Learning_Across_Domains)] [101]
- [ICCV 2021
- [MICCAI 2021
- [ICONIP 2021
- [ICCV 2021
- [ICCV 2021
- [109 - 009-5152-4.pdf))] [112], domain generalization [113] aims to learn a model from source domain(s) and make it generalize well to unknown target domains.
- [109 - 009-5152-4.pdf))] [112], domain generalization [113] aims to learn a model from source domain(s) and make it generalize well to unknown target domains.
Federated Domain Generalization
Neural Architecture Search-based Methods
- [CVPR 2021 - ELCFS)] (FedDG) [147]
- [ICCV 2021
- [arXiv 2021
Open/Heterogeneous Domain Generalization
Neural Architecture Search-based Methods
- [ECCV 2020
- [CVPR 2021 - DAML)] (DAML) [119]