The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Open accessAbstract
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions. Previous works may not effectively uncover the underlying reasons that would lead to the drastic model degradation on the target task. In this paper, we empirically reveal that the erratic discrimination of the target domain mainly stems from its much smaller feature norms with respect to that of the source domain. To this end, we propose a novel parameter-free Adaptive Feature Norm approach. We demonstrate that progressively adapting the feature norms of the two domains to a large range of values can result in significant transfer gains, implying that those task-specific features with larger norms are more transferable. Our method successfully unifies the computation of both standard and partial domain adaptation with more robustness against the negative transfer issue. Without bells and whistles but a few lines of code, our method substantially lifts the performance on the target task and exceeds state-of-the-arts by a large margin (11.5% on Office-Home and 17.1% on VisDA2017). We hope our simple yet effective approach will shed some light on the future research of transfer learning. Code is available at https://github.com/jihanyang/AFN.
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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
Transfer adaptation learning: A decade survey
Zhang, L., & Gao, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183326 · Cited by 314
Generalized Source-free Domain Adaptation
Yang, S., Wang, Y., van de Weijer, J., Herranz, L., & Jui, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00885 · Cited by 267
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Liu, B., Li, Y., Liu, Y., Guo, Y., & Chen, X. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432208 · Cited by 95
Metadata
- Authors
- Xu, R., Li, G., Yang, J., & Lin, L.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 1426-1435
- OpenAlex
- W2982204955
- Cited by
- 508
- References
- 52
- FWCI
- 26.04
- Type
- conference paper
- Language
- en
- Open access
- green
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