A NOVEL INFORMATION-THEORETIC EXPLANATION OF GENERATIVE ADVERSARIAL NETWORKS

Content Files

SP22-ECE499Thesis-Shen, Haochen.pdf

Permalink

https://hdl.handle.net/2142/114984

Title: A NOVEL INFORMATION-THEORETIC EXPLANATION OF GENERATIVE ADVERSARIAL NETWORKS
Author(s): Chen, Haochen
Issue Date: 2022-05
Keyword(s): Generative adversarial networks; channel capacity; unsupervised learning
Abstract: Generative Adversarial Network (GAN) is widely used for instance generation in multiple fields. It models a zero-sum game between a generator network and a discriminator network while using theJensen-Shannon divergence between the target distribution and the generated one as the optimization goal. We propose an alternative explanation of such a framework using a binary-input channel.We also show that our proposed goal is equivalent to the original Jensen-Shannon divergence whileenforcing a tighter performance bound. We then introduce an additional trainable parameter πwith only few modification on the vanilla implementation of GAN. We further experiment with ourproposed variant of GAN on MNIST and CIFAR-10, producing ∼ 10 less on the Fr´echet inceptiondistance and ∼ 0.5 more on the inception score.
Type of Resource: text
Genre of Resource: dissertation/thesis
Language: en
Handle URL: https://hdl.handle.net/2142/114984

The best of ECE undergraduate research