In this article, we mathematically study several GAN related topics,
including Inception score, label smoothing, gradient vanishing and the
-log(D(x)) alternative.
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An advanced version is included in arXiv:1703.02000 "Activation Maximization
Generative Adversarial Nets".
Please refer Section 6 in 1703.02000 for detailed analysis on Inception
Score, and refer its appendix for the discussions on Label Smoothing, Gradient
Vanishing and -log(D(x)) Alternative.

本文中我们通过数学方法研究了多个与 GAN 相关的主题，包括 Inception 分数、标签平滑、梯度消失和 - log (D (x)) 替代方案。

Inception 评分、标签平滑、梯度消失和 - log (D (x)) 的替代方案

Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x))  Alternative

Class labels have been empirically shown useful in improving the sample
quality of generative adversarial nets (GANs). In this paper, we mathematically
study the properties of the current variants of GANs that make use of class
label information. With class aware gradient and cross-entropy decomposition,
we reveal how class labels and associated losses influence GAN's training.
Based on that, we propose Activation Maximization Generative Adversarial
Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been
conducted to validate our analysis and evaluate the effectiveness of our
solution, where AM-GAN outperforms other strong baselines and achieves
state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we
demonstrate that, with the Inception ImageNet classifier, Inception Score
mainly tracks the diversity of the generator, and there is, however, no
reliable evidence that it can reflect the true sample quality. We thus propose
a new metric, called AM Score, to provide a more accurate estimation of the
sample quality. Our proposed model also outperforms the baseline methods in the
new metric.

本文系统研究了当前使用类别标签的生成对抗网络 GANs 以及相关的分类信息对 GAN 网络的训练和生成效果的影响，并提出了一种新的基于激活最大化的 AM-GAN 生成对抗网络方案，在 CIFAR-10 数据集上实现了 8.91 的 state-of-the-art Inception 得分，并提出 AM 得分评价指标以更准确地评估生成样本质量。