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Reducing Domain Gap by Reducing Style Bias
[article]
2021
arXiv
pre-print
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic
arXiv:1910.11645v4
fatcat:ugujqj3jxrffjicyz5oouv5gna