自动搜索GAN架构，提升生成质量与稳定性。
Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
- 用进化算法和梯度法自动搜寻最优GAN结构。
- 新方法在多个数据集上超越传统手工设计，提升生成效果。
- 适合想优化GAN架构的研究者参考。
神经架构搜索（NAS）已成为优化生成对抗网络（GAN）设计的关键技术，可自动化寻找高效架构，解决人工设计带来的挑战。本文系统综述了应用于GAN的NAS方法，依据搜索策略、评估指标和性能表现进行分类与比较。研究表明，NAS能显著提升GAN的生成质量、训练稳定性和计算效率；其中进化算法与基于梯度的方法在特定场景下表现更优；同时强调需采用超越Inception Score（IS）与Fréchet Inception Distance（FID）的稳健评估指标，并使用多样化数据集验证性能。通过结构化对比现有NAS-GAN技术，本文旨在为研究人员提供方向，推动更高效的NAS方法发展与GAN领域进步。
原文摘要 · Abstract (English)
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.
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