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Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, Chen Change Loy
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce. This is because most learning algorithms strongly rely on the i.i.d. assumption on source/target data, which is often violated in practice due to domain shift. Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning. Over the last ten years, research in DG has made great progress, leading to a broad spectrum of methodologies, e.g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, to name a few; DG has also been studied in various application areas including computer vision, speech recognition, natural language processing, medical imaging, and reinforcement learning. In this paper, for the first time a comprehensive literature review in DG is provided to summarize the developments over the past decade. Specifically, we first cover the background by formally defining DG and relating it to other relevant fields like domain adaptation and transfer learning. Then, we conduct a thorough review into existing methods and theories. Finally, we conclude this survey with insights and discussions on future research directions.
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Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022). Domain Generalization: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4), 1–20. https://doi.org/10.1109/tpami.2022.3195549
Zhou, Kaiyang, et al. "Domain Generalization: A Survey." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, 2022, pp. 1–20, https://doi.org/10.1109/tpami.2022.3195549.
Zhou, Kaiyang, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. "Domain Generalization: A Survey." IEEE Transactions on Pattern Analysis and Machine Intelligence 45, no. 4 (2022): 1–20. https://doi.org/10.1109/tpami.2022.3195549.
Zhou K., Liu Z., Qiao Y. [и др.] Domain Generalization: A Survey // IEEE Transactions on Pattern Analysis and Machine Intelligence. — 2022. — Vol. 45, № 4. — P. 1–20. — URL: https://doi.org/10.1109/tpami.2022.3195549
@article{zhou2022domain,
title = {{Domain Generalization: A Survey}},
author = {Kaiyang Zhou and Ziwei Liu and Yu Qiao and Tao Xiang and Chen Change Loy},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2022},
volume = {45},
number = {4},
pages = {1--20},
doi = {10.1109/tpami.2022.3195549},
url = {https://doi.org/10.1109/tpami.2022.3195549}
}TY - JOUR AU - Zhou, Kaiyang AU - Liu, Ziwei AU - Qiao, Yu AU - Xiang, Tao AU - Loy, Chen Change TI - Domain Generalization: A Survey JO - IEEE Transactions on Pattern Analysis and Machine Intelligence VL - 45 IS - 4 SP - 1 EP - 20 PY - 2022 DA - 2022/01/01 DO - 10.1109/tpami.2022.3195549 UR - https://doi.org/10.1109/tpami.2022.3195549 ER -
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