# Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis

arXiv: https://arxiv.org/abs/2606.26169 · HTML: https://mlsanity.com/paper/2606.26169/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 60 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [AutoGAN: Neural Architecture Search for Generative Adversarial Networks](https://mlsanity.com/paper/1908.03835/) (2019)
- [Data Augmentation Using GANs](https://mlsanity.com/paper/1904.09135/) (2019)
- [Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1803.00657/) (2018)
- [StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation](https://mlsanity.com/paper/2112.08493/) (2021)
- [AdversarialNAS: Adversarial Neural Architecture Search for GANs](https://mlsanity.com/paper/1912.02037/) (2019)
- [Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search](https://mlsanity.com/paper/2007.09180/) (2020)
- [COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks](https://mlsanity.com/paper/1912.06180/) (2019)
- [Spatial Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1905.12702/) (2019)
- [AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks](https://mlsanity.com/paper/2006.09134/) (2020)
- [Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2006.14208/) (2020)
- [Towards Distributed Coevolutionary GANs](https://mlsanity.com/paper/1807.08194/) (2018)
- [EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs](https://mlsanity.com/paper/2111.15097/) (2021)
- [DEGAS: Differentiable Efficient Generator Search](https://mlsanity.com/paper/1912.00606/) (2019)
- [Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm](https://mlsanity.com/paper/1809.10244/) (2018)

## Abstract (arXiv)

Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the search for effective architectures while addressing the challenges inherent in manual design. This paper provides a comprehensive review of NAS methods applied to GANs, categorizing and comparing various approaches based on criteria such as search strategies, evaluation metrics, and performance outcomes. The review highlights the benefits of NAS in improving GAN performance, stability, and efficiency, while also identifying limitations and areas for future research. Key findings include the superiority of evolutionary algorithms and gradient-based methods in certain contexts, the importance of robust evaluation metrics beyond traditional scores like Inception Score (IS) and Fr'echet Inception Distance (FID), and the need for diverse datasets in assessing GAN performance. By presenting a structured comparison of existing NAS-GAN techniques, this paper aims to guide researchers in developing more effective NAS methods and advancing the field of GANs.