272 citations · 335 across the 6 of their papers we have counts for
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cs.LG2019★ 1 cited
Understanding the Disharmony between Weight Normalization Family and Weight Decay: shifted Regularizer
Li Xiang, Chen Shuo, Xia Yan +1
The merits of fast convergence and potentially better performance of the weight normalization family have drawn increasing attention in recent years. These methods use standardizat…
cs.LG2018
Adversarial Metric Learning
Shuo Chen, Chen Gong, Jian Yang +3
In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results…
cs.LG2018★ 39 cited
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
Xiang Li, Shuo Chen, Xiaolin Hu +1
This paper first answers the question "why do the two most powerful techniques Dropout and Batch Normalization (BN) often lead to a worse performance when they are combined togethe…