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Expand Up @@ -102,6 +102,7 @@ There are mainly two types of distribution shift: domain shift (testing on unsee
- [ICLR 2022] **Gradient Matching for Domain Generalization** [[paper]](https://arxiv.org/pdf/2104.09937)
- [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)
- [ECCV 2022] **Domain Generalization By Mutual-Information Regularization With Pre-Trained Models** [[paper]](https://arxiv.org/pdf/2203.10789)
- [ECCV 2022] **Cross-Domain Ensemble Distillation for Domain Generalization ** [[paper]](https://arxiv.org/pdf/2211.14058)
- [ICML 2022] **Dna: Domain Generalization With Diversified Neural Averaging** [[paper]](https://proceedings.mlr.press/v162/chu22a/chu22a.pdf)
- [NeurIPS 2022] **Domain Generalization Without Excess Empirical Risk** [[paper]](https://openreview.net/pdf?id=pluyPFTiTeJ)
- [ICCV 2023] **PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization** [[paper]](https://arxiv.org/abs/2307.15199)
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