# Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1406.2661 · HTML: https://mlsanity.com/paper/1406.2661/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2014.

## Built on by (most cited first, 30 of 33)

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- [NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models](https://mlsanity.com/paper/2410.00712/) (2024)
- [Deep Learning for Accelerated and Robust MRI Reconstruction: a Review](https://mlsanity.com/paper/2404.15692/) (2024)
- [Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching](https://mlsanity.com/paper/2510.18328/) (2025)
- [Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling](https://mlsanity.com/paper/2604.28185/) (2026)
- [Adversarial Training for Process Reward Models](https://mlsanity.com/paper/2511.22888/) (2025)
- [Towards Agentic Intelligence for Materials Science](https://mlsanity.com/paper/2602.00169/) (2026)
- [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [INSIGHT: An Interpretable Neural Vision-Language Framework for Reasoning of Generative Artifacts](https://mlsanity.com/paper/2511.22351/) (2025)
- [CoCoDiff: Correspondence-Consistent Diffusion Model for Fine-grained Style Transfer](https://mlsanity.com/paper/2602.14464/) (2026)
- [Calorimeter Shower Superresolution with Conditional Normalizing Flows: Implementation and Statistical Evaluation](https://mlsanity.com/paper/2603.26813/) (2026)
- [GMA-SAWGAN-GP: A Novel Data Generative Framework to Enhance IDS Detection Performance](https://mlsanity.com/paper/2603.28838/) (2026)
- [A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models](https://mlsanity.com/paper/2605.06829/) (2026)
- [Neural operator discovery from heterogeneous trajectories](https://mlsanity.com/paper/2607.23337/) (2026)
- [A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design](https://mlsanity.com/paper/2608.18279/) (2026)
- [Learning to Ask Conversational Questions by Optimizing Levenshtein Distance](https://mlsanity.com/paper/2106.15903/) (2021)
- [Accelerating GAN training using highly parallel hardware on public cloud](https://mlsanity.com/paper/2111.04628/) (2021)
- [Innovation and Word Usage Patterns in Machine Learning](https://mlsanity.com/paper/2311.03633/) (2023)
- [Adversarial Reweighting with α-Power Maximization for Domain Adaptation](https://mlsanity.com/paper/2404.17275/) (2024)
- [CtrlNeRF: The Generative Neural Radiation Fields for the Controllable Synthesis of High-fidelity 3D-Aware Images](https://mlsanity.com/paper/2412.00754/) (2024)
- [From Words to Workflows: Automating Business Processes](https://mlsanity.com/paper/2412.03446/) (2024)
- [SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2412.10178/) (2024)
- [Generative Face Parsing Map Guided 3D Face Reconstruction Under Occluded Scenes](https://mlsanity.com/paper/2412.18920/) (2024)
- [Reference-Guided Diffusion Inpainting For Multimodal Counterfactual Generation](https://mlsanity.com/paper/2507.23058/) (2025)
- [AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning](https://mlsanity.com/paper/2604.07900/) (2026)
- [MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion](https://mlsanity.com/paper/2607.14249/) (2026)

## Abstract (arXiv)

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.