[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients

[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients This document discusses several mathematical concepts related to probability and statistics: - It defines the Kullback-Leibler divergence KL(P||Q) as a measure of how one probability distribution P differs from a second distribution Q. - It presents an equation for Qθ(y|x) as a Gaussian distribution with mean fθ(x) and varian

[elu_18]

KLではサポートが重ならないと学習できない,Wassersteinでは勾配推定が偏る問題があったが距離尺度の満たすべき性質を議論しCramer距離を機械学習の諸問題に適用することを提案。GANにおいて生成画像の多様性を改善。 https://