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 show that by restricting the discriminator's loss to be no less than the 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出了卓越的性能。然而，GAN的训练存在不稳定的问题。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。本文聚焦于直接正则化对抗损失函数。我们提出了一种方法，将监督学习中的过拟合抑制方法——浸没问题（Flooding）应用于GANs，以直接阻止判别器的损失降至过低水平。浸没问题需要调整浸没水平（flood level），但当应用于GANs时，我们提出浸没水平的适当设置范围由对抗损失函数决定，这一结论基于使用二元交叉熵损失的GANs理论分析。我们实验验证了浸没问题能够稳定GAN训练，并可与其他稳定技术相结合。我们还证明，通过将判别器的损失限制在不低于浸没水平，即使浸没水平设置较高，训练也能稳定进行。