Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies

Abstract

Despite its short history, Generative Adversarial Network (GAN) has been extensively studied and used for various tasks, including its original purpose, i.e., synthetic sample generation. However, applying GAN to different data types with diverse neural network architectures has been hindered by its limitation in training, where the model easily diverges. Such a notorious training of GANs is well known and has been addressed in numerous studies. Consequently, in order to make the training of GAN stable, numerous regularization methods have been proposed in recent years. This paper reviews the regularization methods that have been recently introduced, most of which have been published in the last three years. Specifically, we focus on general methods that can be commonly used regardless of neural network architectures. To explore the latest research trends in the regularization for GANs, the methods are classified into several groups by their operation principles, and the differences between the methods are analyzed. Furthermore, to provide practical knowledge of using these methods, we investigate popular methods that have been frequently employed in state-of-the-art GANs. In addition, we discuss the limitations in existing methods and propose future research directions.

References in corpus (22)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Instance Normalization: The Missing Ingredient for Fast Stylization
• Generative Adversarial Network in Medical Imaging: A Review
• Deep Facial Expression Recognition: A Survey
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
• Energy-based Generative Adversarial Network
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
• MaskGAN: Better Text Generation via Filling in the______
• BAGAN: Data Augmentation with Balancing GAN
• Variational Approaches for Auto-Encoding Generative Adversarial Networks
• Mode Regularized Generative Adversarial Networks
• On Convergence and Stability of GANs
• Spectral Norm Regularization for Improving the Generalizability of Deep Learning
• On the regularization of Wasserstein GANs
• Hierarchical Implicit Models and Likelihood-Free Variational Inference
• Lipschitz Generative Adversarial Nets
• Score-Guided Generative Adversarial Networks

Cited by in corpus (6)

• A survey of synthetic data augmentation methods in computer vision
• A Systematic Survey of Regularization and Normalization in GANs
• An Integral Projection-based Semantic Autoencoder for Zero-Shot Learning
• Score-Guided Generative Adversarial Networks
• Estimation with Uncertainty via Conditional Generative Adversarial Networks
• Game of GANs: Game-Theoretical Models for Generative Adversarial Networks