The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Episodic Training for Domain Generalization
Open accessAbstract
Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. Furthermore, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training a feature for robustness to novel problems. This shows that DG training can benefit standard practice in computer vision.
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Cites
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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
Self-citationA Fourier-based Framework for Domain Generalization
Xu, Q., Zhang, R., Zhang, Y., Wang, Y., & Tian, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01415 · Cited by 561
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A. A., & Hardt, M. (2019) · arXiv · doi:10.48550/arxiv.1909.13231 · Cited by 393
Learning to Generate Novel Domains for Domain Generalization
Zhou, K., Yang, Y., Hospedales, T., & Xiang, T. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58517-4_33 · Cited by 382
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Metadata
- Authors
- Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.-Z., & Hospedales, T.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 1446-1455
- OpenAlex
- W2981368934
- Cited by
- 435
- References
- 41
- FWCI
- 22.74
- Type
- conference paper
- Language
- en
- Open access
- green
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