# Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments

arXiv: https://arxiv.org/abs/2005.13178 · HTML: https://mlsanity.com/paper/2005.13178/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 11 of 11)

- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [Quantifying the uncertainty of model-based synthetic image quality metrics](https://mlsanity.com/paper/2504.03623/) (2025)
- [Generative Adversarial Networks for Malware Detection: a Survey](https://mlsanity.com/paper/2302.08558/) (2023)
- [Feature Alignment as a Generative Process](https://mlsanity.com/paper/2106.12562/) (2021)
- [Multitask Fine-Tuning and Generative Adversarial Learning for Improved Auxiliary Classification](https://mlsanity.com/paper/2408.15265/) (2024)
- [Representation learning from OCT images](https://mlsanity.com/paper/2605.02589/) (2026)
- [Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices](https://mlsanity.com/paper/2607.12599/) (2026)

## Abstract (arXiv)

One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density function (PDF). In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously. The generator learns to generate plausible data, and the discriminator learns to distinguish fake data created by the generator from real data samples. Given the rapid growth of GANs over the last few years and their application in various fields, it is necessary to investigate these networks accurately. In this paper, after introducing the main concepts and the theory of GAN, two new deep generative models are compared, the evaluation metrics utilized in the literature and challenges of GANs are also explained. Moreover, the most remarkable GAN architectures are categorized and discussed. Finally, the essential applications in computer vision are examined.