Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
Summary
This paper provides a comprehensive review of Neural Architecture Search (NAS) methods applied to Generative Adversarial Networks (GANs), categorizing approaches and highlighting benefits and limitations.
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# Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis Source: [https://arxiv.org/abs/2606.26169](https://arxiv.org/abs/2606.26169) [View PDF](https://arxiv.org/pdf/2606.26169) > 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\. ## Submission history From: Abrar Alotaibi \[[view email](https://arxiv.org/show-email/59cbf6e2/2606.26169)\] **\[v1\]**Wed, 24 Jun 2026 08:29:58 UTC \(1,288 KB\)
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