Computer Science > Machine Learning
[Submitted on 4 Jul 2023 (v1), last revised 7 Jul 2023 (this version, v2)]
Title:Stability Analysis Framework for Particle-based Distance GANs with Wasserstein Gradient Flow
View PDFAbstract:In this paper, we investigate the training process of generative networks that use a type of probability density distance named particle-based distance as the objective function, e.g. MMD GAN, Cramér GAN, EIEG GAN. However, these GANs often suffer from the problem of unstable training. In this paper, we analyze the stability of the training process of these GANs from the perspective of probability density dynamics. In our framework, we regard the discriminator $D$ in these GANs as a feature transformation mapping that maps high dimensional data into a feature space, while the generator $G$ maps random variables to samples that resemble real data in terms of feature space. This perspective enables us to perform stability analysis for the training of GANs using the Wasserstein gradient flow of the probability density function. We find that the training process of the discriminator is usually unstable due to the formulation of $\min_G \max_D E(G, D)$ in GANs. To address this issue, we add a stabilizing term in the discriminator loss function. We conduct experiments to validate our stability analysis and stabilizing method.
Submission history
From: Chuqi Chen [view email][v1] Tue, 4 Jul 2023 18:49:34 UTC (1,467 KB)
[v2] Fri, 7 Jul 2023 05:04:37 UTC (1,473 KB)
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