AutoGAN: An Automated Human-Out-of-the-Loop Approach for Training Generative Adversarial Networks

Abstract

Generative Adversarial Networks (GANs) have been used for many applications with overwhelming success. The training process of these models is complex, involving a zero-sum game between two neural networks trained in an adversarial manner. Thus, to use GANs, researchers and developers need to answer the question: “Is the GAN sufficiently trained?”. However, understanding when a GAN is well trained for a given problem is a challenging and laborious task that usually requires monitoring the training process and human intervention for assessing the quality of the GAN generated outcomes. Currently, there is no automatic mechanism for determining the required number of epochs that correspond to a well-trained GAN, allowing the training process to be safely stopped. In this paper, we propose AutoGAN, an algorithm that allows one to answer this question in a fully automatic manner with minimal human intervention, being applicable to different data modalities including imagery and tabular data. Through an extensive set of experiments, we show the clear advantage of our solution when compared against alternative methods, for a task where the GAN outputs are used as an oversampling method. Moreover, we show that AutoGAN not only determines a good stopping point for training the GAN, but it also allows one to run fewer training epochs to achieve a similar or better performance with the GAN outputs.

Keywords

• generative adversarial models
• automatic training