Online Kernel based Generative Adversarial Networks

• URL: http://arxiv.org/abs/2006.11432v1
• Date: Fri, 19 Jun 2020 22:54:01 GMT
• Title: Online Kernel based Generative Adversarial Networks
• Authors: Yeojoon Youn, Neil Thistlethwaite, Sang Keun Choe, Jacob Abernethy
• Abstract summary: We show that Online Kernel-based Generative Adversarial Networks (OKGAN) mitigate a number of training issues, including mode collapse and cycling. OKGANs empirically perform dramatically better, with respect to reverse KL-divergence, than other GAN formulations on synthetic data.
• Score: 0.45880283710344055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the major breakthroughs in deep learning over the past five years has been the Generative Adversarial Network (GAN), a neural network-based generative model which aims to mimic some underlying distribution given a dataset of samples. In contrast to many supervised problems, where one tries to minimize a simple objective function of the parameters, GAN training is formulated as a min-max problem over a pair of network parameters. While empirically GANs have shown impressive success in several domains, researchers have been puzzled by unusual training behavior, including cycling so-called mode collapse. In this paper, we begin by providing a quantitative method to explore some of the challenges in GAN training, and we show empirically how this relates fundamentally to the parametric nature of the discriminator network. We propose a novel approach that resolves many of these issues by relying on a kernel-based non-parametric discriminator that is highly amenable to online training---we call this the Online Kernel-based Generative Adversarial Networks (OKGAN). We show empirically that OKGANs mitigate a number of training issues, including mode collapse and cycling, and are much more amenable to theoretical guarantees. OKGANs empirically perform dramatically better, with respect to reverse KL-divergence, than other GAN formulations on synthetic data; on classical vision datasets such as MNIST, SVHN, and CelebA, show comparable performance.

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