Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative
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In this paper, we study several GAN related topics mathematically, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. We show that Inception score is actually equivalent to Mode score, both consisting of two entropy terms, which has the drawback of ignoring the prior distribution of the labels. We thus propose AM score as an alternative that leverages cross-entropy and takes the reference distribution into account. Empirical results indicate that AM score outperforms Inception score. We study label smoothing, gradient vanishing and -log(D(x)) alternative from the perspective of class-aware gradient, with which we show the exact problems when applying label smoothing to fake samples along with the log(1-D(x)) generator loss, which is previously unclear, and more importantly show that the problem does not exist when using the -log(D(x)) generator loss.
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