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Difference in Euclidean Norm Can Cause Semantic Divergence in Batch Normalization. (arXiv:2207.02625v1 [cs.CV])
July 7, 2022, 1:12 a.m. | Zhennan Wang, Kehan Li, Runyi Yu, Yian Zhao, Pengchong Qiao, Guoli Song, Fan Xu, Jie Chen
cs.CV updates on arXiv.org arxiv.org
In this paper, we show that the difference in Euclidean norm of samples can
make a contribution to the semantic divergence and even confusion, after the
spatial translation and scaling transformation in batch normalization. To
address this issue, we propose an intuitive but effective method to equalize
the Euclidean norms of sample vectors. Concretely, we $l_2$-normalize each
sample vector before batch normalization, and therefore the sample vectors are
of the same magnitude. Since the proposed method combines the $l_2$
normalization …
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