Generative Adversarial Networks (GANs) are a type of generative model which
have received much attention due to their ability to model complex real-world
data. Despite their recent successes, the process of training GANs remains
challenging, suffering from instability problems such as non-convergence,
vanishing or exploding gradients, and mode collapse. In recent years, a diverse
set of approaches have been proposed which focus on stabilizing the GAN
training procedure. The purpose of this survey is to provide a comprehensive
overview of the GAN training stabilization methods which can be found in the
literature. We discuss the advantages and disadvantages of each approach, offer
a comparative summary, and conclude with a discussion of open problems.

本文综述了文献中可找到的 GAN 训练稳定方法，讨论了每种方法的优缺点并进行比较总结，最后提出了未解决问题的讨论。

稳定生成对抗网络：综述

Stabilizing Generative Adversarial Networks: A Survey

This paper advances the design of CTC-based all-neural (or end-to-end) speech
recognizers. We propose a novel symbol inventory, and a novel iterated-CTC
method in which a second system is used to transform a noisy initial output
into a cleaner version. We present a number of stabilization and initialization
methods we have found useful in training these networks. We evaluate our system
on the commonly used NIST 2000 conversational telephony test set, and
significantly exceed the previously published performance of similar systems,
both with and without the use of an external language model and decoding
technology.

本文提出了一种基于 CTC 的全神经网络语音识别器的设计方法，包括新的符号存储库、基于迭代的 CTC 方法、稳定化方法和初始化方法等，使用 NIST 2000 会话电话测试集进行了评估，结果表明该系统明显超过了先前发布的相似系统的性能，无需使用外部语言模型和解码技术。