Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
This paper provides a comprehensive review of Neural Architecture Search (NAS) methods applied to Generative Adversarial Networks (GANs), comparing search strategies, evaluation metrics, and performance outcomes. It highlights the superiority of evolutionary and gradient-based methods in certain contexts, the need for robust evaluation metrics beyond IS and FID, and the importance of diverse datasets.
[2606.26169] Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
[Submitted on 24 Jun 2026]
Title:Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
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Abstract: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 Fréchet 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.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26169 [cs.LG]
(or arXiv:2606.26169v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.26169
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Applied Sciences, 15(7), 3623 (2025)
Related DOI:
https://doi.org/10.3390/app15073623
DOI(s) linking to related resources
Submission history
From: Abrar Alotaibi [view email] [v1] Wed, 24 Jun 2026 08:29:58 UTC (1,288 KB)
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