Analyzing and Improving Generative Adversarial Training for Generative Modeling and Out-of-Distribution Detection

Xuwang Yin, Shiying li, Gustavo Rohde

Keywords: Adversarial Training, Generative Modeling, Out-of-Distribution Detection, GANs, Generative adversarial networks

Abstract: Generative adversarial training (GAT) is a recently introduced adversarial defense method. Previous works have focused on empirical evaluations of its application to training robust predictive models. In this paper we focus on theoretical understanding of the GAT method and extending its application to generative modeling and out-of-distribution detection. We analyze the optimal solutions of the maximin formulation employed by the GAT objective, and make a comparative analysis of the minimax formulation employed by GANs. We use theoretical analysis and 2D simulations to understand the convergence property of the training algorithm. Based on these results, we develop an unconstrained GAT algorithm, and conduct comprehensive evaluations of the algorithm's application to image generation and adversarial out-of-distribution detection. Our results suggest that generative adversarial training is a promising new direction for the above applications.

One-sentence Summary: Theoretical understanding of the generative adversarial training method and extending its application to generative modeling and out-of-distribution detection

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Reviewed Version (pdf): https://openreview.net/references/pdf?id=6Z09ydXbEv