条件gan的多类铰链损失

Ilya Kavalerov, W. Czaja
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引用次数: 18

摘要

我们提出了一种新的算法,通过对常用的铰链损失进行多类泛化,将类条件信息整合到gan的批评中,该算法兼容监督和半监督设置。我们研究了同时训练一个最先进的生成器和一个准确的分类器之间的折衷,并提出了一种使用我们的算法来衡量生成器和评论家在多大程度上是类条件的方法。我们展示了生成器-评论家对尊重类条件输入和生成最高质量图像之间的权衡。通过我们的多铰链损失修改,我们能够提高Imagenet数据集上的Inception Scores和Frechet Inception Distance。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Class Hinge Loss for Conditional GANs
We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings. We study the compromise between training a state of the art generator and an accurate classifier simultaneously, and propose a way to use our algorithm to measure the degree to which a generator and critic are class conditional. We show the trade-off between a generator-critic pair respecting class conditioning inputs and generating the highest quality images. With our multi-hinge loss modification we are able to improve Inception Scores and Frechet Inception Distance on the Imagenet dataset.
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