# Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

arXiv: https://arxiv.org/abs/1906.01529 · HTML: https://mlsanity.com/paper/1906.01529/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 12 of 12)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Copula Flows for Synthetic Data Generation](https://mlsanity.com/paper/2101.00598/) (2021)
- [AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention](https://mlsanity.com/paper/2101.05682/) (2021)
- [Advancing biological super-resolution microscopy through deep learning: a brief review](https://mlsanity.com/paper/2106.13064/) (2021)
- [Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images](https://mlsanity.com/paper/2101.11600/) (2021)
- [Neuroplastic graph attention networks for nuclei segmentation in histopathology images](https://mlsanity.com/paper/2201.03669/) (2022)
- [Physically-admissible polarimetric data augmentation for road-scene analysis](https://mlsanity.com/paper/2206.07431/) (2022)
- [Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixer](https://mlsanity.com/paper/2309.08129/) (2023)
- [Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition](https://mlsanity.com/paper/2101.10845/) (2021)
- [Learning Neural Models for Natural Language Processing in the Face of Distributional Shift](https://mlsanity.com/paper/2109.01558/) (2021)
- [Multi-perspective Feedback-attention Coupling Model for Continuous-time Dynamic Graphs](https://mlsanity.com/paper/2312.07983/) (2023)
- [Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain](https://mlsanity.com/paper/2402.06673/) (2024)

## Abstract (arXiv)

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are: (1) the generation of high quality images, (2) diversity of image generation, and (3) stable training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state of the art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress towards important computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Code related to GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.