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arXiv · 2102.04593

Regularized Generative Adversarial Network

Gabriele Di Cerbo·Ali Hirsa·Ahmad Shayaan
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Abstract

We propose a framework for generating samples from a probability distribution that differs from the probability distribution of the training set. We use an adversarial process that simultaneously trains three networks, a generator and two discriminators. We refer to this new model as regularized generative adversarial network (RegGAN). We evaluate RegGAN on a synthetic dataset composed of gray scale images and we further show that it can be used to learn some pre-specified notions in topology (basic topology properties). The work is motivated by practical problems encountered while using generative methods in the art world.

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Gabriele Di Cerbo, Ali Hirsa, Ahmad Shayaan. 2021-02-09. Regularized Generative Adversarial Network. https://arxiv.org/abs/2102.04593

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