Generative Adversarial Network 3 WGAN/ EBGAN
前提回顾 JS 散度 Jensen-Shannon Divergence 解决了两个概率分布的相似度，值0-1之间 但是如果P,Q离得很远，完全没有重叠的时候，KL散度值是没有意义的，JS散度值是个常数，这就意味着这一点梯度为0. JS divergence is not suitable in most case，PGandPdata are not overlapped 1.PGandPdat
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相关文章
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