The Six Fronts of the Generative Adversarial Networks

2019/10/29 by Alceu Bissoto, Eduardo Valle, Bissoto, Alceu +3
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1910.13076

openalex publication_date 2019/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Generative Adversarial Networks fostered a newfound interest in generative models, resulting in a swelling wave of new works that new-coming researchers may find formidable to surf. In this paper, we intend to help those researchers, by splitting that incoming wave into six "fronts": Architectural Contributions, Conditional Techniques, Normalization and Constraint Contributions, Loss Functions, Image-to-image Translations, and Validation Metrics. The division in fronts organizes literature into approachable blocks, ultimately communicating to the reader how the area is evolving. Previous surveys in the area, which this works also tabulates, focus on a few of those fronts, leaving a gap that we propose to fill with a more integrated, comprehensive overview. Here, instead of an exhaustive survey, we opt for a straightforward review: our target is to be an entry point to this vast literature, and also to be able to update experienced researchers to the newest techniques.

Citations

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Auto-Encoding Variational Bayes
• Gradient-based learning applied to document recognition
• Pixel Recurrent Neural Networks
• Conditional Image Synthesis With Auxiliary Classifier GANs
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Boundary-Seeking Generative Adversarial Networks
• Triple Generative Adversarial Nets
• The Cramer Distance as a Solution to Biased Wasserstein Gradients
• Generative Adversarial Networks: An Overview
• Self-Attention Generative Adversarial Networks
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Conditional Image Generation with PixelCNN Decoders
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Related

• Comparative Study on Generative Adversarial Networks
• NIPS 2016 Tutorial: Generative Adversarial Networks
• A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision
• Generative adversarial networks
• Generative Adversarial Networks (GANs): What it can generate and What it cannot?