Comprehensive Guide to Generative Adversarial Networks and Wasserstein GANs

Prajjwal
Prajjwal
May 23, 2018 · 8 min read
Progressive growing of GANs

Overview

Introduction

What is Earth Mover’s Distance?

Kullback–Leibler and Jensen–Shannon Divergence

Generative Adversarial Network (GAN)

Use Wasserstein Distance as GAN Loss Function

Improved GAN Training

Overview of DCGAN

Problem with GANs

Evaluation Metric

Few GANs Applications

Code

Empirical Results

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Prajjwal

Bringing clarity to AI Research

AI Journal

Covering AI Research and making it accessible