A survey on GANs for computer vision: Recent research, analysis and taxonomy

Abstract

In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought, the community is constantly coming up with new researches that make it almost impossible to keep up with the times. Our survey aims to provide a general overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas of the most widely recognized variants. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area; as a vital part of the process, the different metrics for evaluating the performance of GANs and the frequently used loss functions will be analyzed. The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.

77 pages, 11 figures, 4 tables

References in corpus (18)

• Conditional Generative Adversarial Nets
• Generative Adversarial Networks
• Striving for Simplicity: The All Convolutional Net
• Diffusion Models Beat GANs on Image Synthesis
• Diffusion Models in Vision: A Survey
• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
• Alias-Free Generative Adversarial Networks
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• The emerging commercial landscape of quantum computing
• Improving Generalization and Stability of Generative Adversarial Networks
• Approximation and Convergence Properties of Generative Adversarial Learning
• GAN and VAE from an Optimal Transport Point of View
• Variational autoencoder reconstruction of complex many-body physics
• GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint
• MISS GAN: A Multi-IlluStrator Style Generative Adversarial Network for image to illustration translation
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images
• Dynamics of Fourier Modes in Torus Generative Adversarial Networks
• Video Deblurring by Fitting to Test Data

Cited by in corpus (2)

• Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art
• Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement from CT Scans