Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
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 efficien
The proliferation of complex GAN architectures and the computational demands of their design make automated search methods increasingly relevant, propelling this area of research.
This review consolidates the progress in automating GAN design, indicating a maturing field that will accelerate the development and application of advanced AI models.
The efficiency and quality of generative AI model development will improve, moving from bespoke manual engineering to systematized architectural optimization.
- · AI researchers and developers
- · Creative industries using generative AI
- · Generative AI platform providers
- · Compute infrastructure providers (for NAS)
- · Manual GAN architecture designers (relative to automated systems)
- · Companies without access to advanced NAS techniques
More sophisticated and stable generative models become available across various applications.
Reduced development time and cost for new generative AI applications, increasing market accessibility.
Proliferation of highly realistic synthetic media, potentially complicating content authentication and trust online.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG