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

Generative Adversarial Networks (GANs) are powerful tools for data augmentation and generative modeling, but they are notoriously difficult to design and train. Manual architecture selection requires significant expert knowledge, and the adversarial training process is prone to instability and mode collapse. Neural Architecture Search (NAS) has emerged as a solution to automate this process, allowing for the discovery of optimized network structures without the need for manual trial-and-error.

This paper provides a comprehensive review of NAS applications in GANs, synthesizing research from 2021 to 2025. The authors developed a structured comparative framework to categorize existing techniques based on search strategies (e.g., reinforcement learning, evolutionary algorithms, and gradient-based methods), search spaces, and evaluation metrics. By moving beyond early studies that focused only on the generator, this review evaluates modern approaches that optimize both the generator and the discriminator simultaneously.

The review highlights that while NAS-GANs have successfully improved image quality and computational efficiency, the field faces significant hurdles. A major finding is the superiority of evolutionary and gradient-based search strategies in specific contexts. However, the authors identify a critical lack of consistency in how these models are evaluated. Traditional metrics like the Inception Score (IS) and Fréchet Inception Distance (FID) are often insufficient to capture the full performance profile of a GAN. Furthermore, the reliance on narrow, specific datasets limits the generalizability of many proposed architectures. The authors emphasize that future research must prioritize reproducibility, the use of more diverse datasets, and the development of more robust evaluation criteria.