A Mathematical Introduction to Generative Adversarial Nets (GAN)
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Abstract (arXiv)
Generative Adversarial Nets (GAN) have received considerable attention since the 2014 groundbreaking work by Goodfellow et al. Such attention has led to an explosion in new ideas, techniques and applications of GANs. To better understand GANs we need to understand the mathematical foundation behind them. This paper attempts to provide an overview of GANs from a mathematical point of view. Many students in mathematics may find the papers on GANs more difficulty to fully understand because most of them are written from computer science and engineer point of view. The aim of this paper is to give more mathematically oriented students an introduction to GANs in a language that is more familiar to them.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 1.
8 papers.
- 2021Regularising Inverse Problems with Generative Machine Learning Modelscited by 22
- 2022Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoningcited by 16
- 2021Convergence of Deep ReLU Networkscited by 14
- 2021Convergence of Deep Convolutional Neural Networkscited by 13
- 2023Uniform Convergence of Deep Neural Networks with Lipschitz Continuous Activation Functions and Variable Widthscited by 5
- 2021Training Generative Adversarial Networks with Adaptive Composite Gradientcited by 1
- 2022Convergence Analysis of Deep Residual Networkscited by 1
- 2022Convergence of Deep Neural Networks with General Activation Functions and Poolingcited by 0
- 2023Uniform Convergence of Deep Neural Networks with Lipschitz Continuous Activation Functions and Variable Widths2 Jun 2023
- 2022Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning28 Oct 2022
- 2022Convergence Analysis of Deep Residual Networks13 May 2022
- 2022Convergence of Deep Neural Networks with General Activation Functions and Pooling13 May 2022
- 2021Training Generative Adversarial Networks with Adaptive Composite Gradient10 Nov 2021
- 2021Convergence of Deep Convolutional Neural Networks28 Sep 2021
- 2021Convergence of Deep ReLU Networks27 Jul 2021
- 2021Regularising Inverse Problems with Generative Machine Learning Models22 Jul 2021