The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Learning to Learn Single Domain Generalization
Open accessAbstract
We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named adversarial domain augmentation to solve this Out-of-Distribution (OOD) generalization problem. The key idea is to leverage adversarial training to create "fictitious" yet "challenging" populations, from which a model can learn to generalize with theoretical guarantees. To facilitate fast and desirable domain augmentation, we cast the model training in a meta-learning scheme and use a Wasserstein Auto-Encoder (WAE) to relax the widely used worst-case constraint. Detailed theoretical analysis is provided to testify our formulation, while extensive experiments on multiple benchmark datasets indicate its superior performance in tackling single domain generalization.
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Cites
Deep Residual Learning for Image Recognition
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Cited by
Domain Generalization: A Survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195549 · Cited by 1,315
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046
Recent Advances in Adversarial Training for Adversarial Robustness
Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021) · arXiv · doi:10.48550/arxiv.2102.01356 · Cited by 490
Recent Advances in Adversarial Training for Adversarial Robustness
Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/591 · Cited by 490
SMIL: Multimodal Learning with Severely Missing Modality
Ma, M., Ren, J., Zhao, L., Tulyakov, S., Wu, C., & Peng, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16330 · Cited by 332
“Other works adapted MAML for domain generalization (Li et al. 2018; Qiao, Zhao, and Peng 2020) and knowledge distillation (Zhao et al. 2020).”
MethodSelf-citation
Metadata
- Authors
- Qiao, F., Zhao, L., & Peng, X.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 12553-12562
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034373371
- Cited by
- 455
- References
- 60
- FWCI
- 23.79
- Type
- conference paper
- Language
- en
- Open access
- green
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