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

arXiv 2606.26169 ↗First posted 24 Jun 2026cs.LG cs.AIcited by 0

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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.

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.

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References · 27

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  12. 2017VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learningcited by 200
15 more
  1. 2023Neural Architecture Search: Insights from 1000 Paperscited by 139
  2. 2019AutoGAN: Neural Architecture Search for Generative Adversarial Networkscited by 94
  3. 2019Data Augmentation Using GANscited by 58
  4. 2018Evolutionary Generative Adversarial Networkscited by 42
  5. 2021StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulationcited by 40
  6. 2019AdversarialNAS: Adversarial Neural Architecture Search for GANscited by 38
  7. 2020Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Searchcited by 25
  8. 2019COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networkscited by 13
  9. 2019Spatial Evolutionary Generative Adversarial Networkscited by 10
  10. 2020AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networkscited by 10
  11. 2020Searching towards Class-Aware Generators for Conditional Generative Adversarial Networkscited by 6
  12. 2018Towards Distributed Coevolutionary GANscited by 5
  13. 2021EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANscited by 5
  14. 2019DEGAS: Differentiable Efficient Generator Searchcited by 3
  15. 2018Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithmcited by 1

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