Regularized Generative Adversarial Network

• URL: http://arxiv.org/abs/2102.04593v1
• Date: Tue, 9 Feb 2021 01:13:36 GMT
• Title: Regularized Generative Adversarial Network
• Authors: Gabriele Di Cerbo, Ali Hirsa, Ahmad Shayaan
• Abstract summary: We propose a framework for generating samples from a probability distribution that differs from the probability distribution of the training set. We refer to this new model as regularized generative adversarial network (RegGAN)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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