Generative Adversarial Networks (GANs) have shown remarkable performance in image generation. However, GAN training suffers from the problem of instability. One of the main approaches to address this problem is to modify the loss function, often using regularization terms in addition to changing the type of adversarial losses. This paper focuses on directly regularizing the adversarial loss function. We propose a method that applies flooding, an overfitting suppression method in supervised learning, to GANs to directly prevent the discriminator's loss from becoming excessively low. Flooding requires tuning the flood level, but when applied to GANs, we propose that the appropriate range of flood level settings is determined by the adversarial loss function, supported by theoretical analysis of GANs using the binary cross entropy loss. We experimentally verify that flooding stabilizes GAN training and can be combined with other stabilization techniques. We also reveal that by restricting the discriminator's loss to be no greater than flood level, the training proceeds stably even when the flood level is somewhat high.

翻译：生成对抗网络（GANs）在图像生成领域展现了卓越性能。然而，GAN训练存在不稳定问题。解决该问题的主要方法之一是修改损失函数，通常在使用正则化项的同时改变对抗损失的类型。本文专注于直接正则化对抗损失函数。我们提出了一种将监督学习中的过拟合抑制方法——洪泛（flooding）应用于GANs的方法，直接防止判别器的损失过低。洪泛需要调整洪泛水平，但当应用于GANs时，我们提出洪泛水平的合适范围由对抗损失函数决定，并基于使用二元交叉熵损失的GANs理论分析提供支撑。我们通过实验验证了洪泛能稳定GAN训练，并可与其他稳定化技术相结合。我们还揭示了将判别器损失限制在不高于洪泛水平时，即使洪泛水平略高，训练也能稳定进行。