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Learning to Optimize Domain Specific Normalization for Domain Generalization

Cited 0 time in Web of Science Cited 81 time in Scopus
Authors

Seo, Seonguk; Suh, Yumin; Kim, Dongwan; Kim, Geeho; Han, Jongwoo; Han, Bohyung

Issue Date
2020-08
Publisher
Springer Science and Business Media Deutschland GmbH
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.12367 LNCS, pp.68-83
Abstract
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per domain. For each domain, the activations are normalized by a weighted average of multiple normalization statistics. The normalization statistics are kept track of separately for each normalization type if necessary. Specifically, we employ batch and instance normalizations in our implementation to identify the best combination of these two normalization methods in each domain. The optimized normalization layers are effective to enhance the generalizability of the learned model. We demonstrate the state-of-the-art accuracy of our algorithm in the standard domain generalization benchmarks, as well as viability to further tasks such as multi-source domain adaptation and domain generalization in the presence of label noise.
ISSN
0302-9743
URI
https://hdl.handle.net/10371/190973
DOI
https://doi.org/10.1007/978-3-030-58542-6_5
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