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May, 2017
Cramer距离作为解决偏置Wasserstein梯度的方案
The Cramer Distance as a Solution to Biased Wasserstein Gradients
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Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan...
TL;DR
本文研究了概率分歧度量的性质,比较了Wasserstein度量与Kullback-Leibler分歧的差异,提出Cramér距离作为一种替代度量并设计了Cramér生成对抗网络,表现显著优于Wasserstein生成对抗网络。
Abstract
The Wasserstein probability metric has received much attention from the
machine learning
community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the
wasserstein metric
re
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