How Generative Adversarial Networks and Their Variants Work: An Overview

Abstract

Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property leads GAN to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation and other academic fields. In this paper, we aim to discuss the details of GAN for those readers who are familiar with, but do not comprehend GAN deeply or who wish to view GAN from various perspectives. In addition, we explain how GAN operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GAN for their research.

41 pages, 16 figures, Published in ACM Computing Surveys (CSUR)

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• Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks
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• Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks
• Training Generative Adversarial Networks with Adaptive Composite Gradient
• Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey
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• A Novel Measure to Evaluate Generative Adversarial Networks Based on Direct Analysis of Generated Images
• Game of GANs: Game-Theoretical Models for Generative Adversarial Networks
• AI Marker-based Large-scale AI Literature Mining
• Improving Stability of LS-GANs for Audio and Speech Signals
• Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System
• Extremal learning: extremizing the output of a neural network in regression problems
• An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator
• Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks