New review analyzes Neural Architecture

Short summary

This arXiv review examines Neural Architecture Search (NAS) techniques for optimizing Generative Adversarial Network (GAN) design, comparing evolutionary algorithms and gradient-based methods. Key findings highlight the importance of robust evaluation metrics beyond Inception Score and Fréchet Inception Distance, plus diverse datasets for assessing GAN performance. The paper guides researchers in developing more effective NAS methods to advance GAN research.

• •Comprehensive review of NAS techniques applied to GAN optimization and design automation
• •Evolutionary and gradient-based methods outperform traditional approaches in specific contexts
• •Robust evaluation metrics and dataset diversity are critical for reliable GAN performance assessment

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