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

2026-06-24

AI Summary

This work presents the first systematic survey and critical analysis of neural architecture search (NAS) methods tailored for generative adversarial networks (GANs), addressing the inefficiency and instability of manual GAN design, which often struggles to balance performance and generalization. The study organizes existing approaches through a structured comparison based on search strategies, evaluation metrics, and empirical performance. It advocates moving beyond conventional Inception Score (IS) and Fréchet Inception Distance (FID) toward more robust evaluation frameworks and diverse datasets. The analysis further highlights the complementary strengths of evolutionary algorithms and gradient-based methods across different scenarios. By clarifying the current limitations and untapped potential of NAS-GAN methodologies, this work establishes a foundation for future research and advances the standardization and performance of automated GAN architecture design.

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.

Problem

• Neural Architecture Search
• Generative Adversarial Networks
• Architecture Optimization
• Automated Design
• GAN Performance

Innovation

• Neural Architecture Search
• Generative Adversarial Networks
• Evolutionary Algorithms
• Gradient-based Search
• Evaluation Metrics