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

A new arXiv paper reviews neural architecture search (NAS) methods for generative adversarial networks (GANs), finding that evolutionary algorithms and gradient-based methods outperform others in optimizing GAN design. The study emphasizes the need for robust evaluation metrics beyond Inception Score and Fréchet Inception Distance, and highlights the importance of diverse datasets for assessing GAN performance.

arXiv:2606.26169v1 Announce Type: new 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 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.