AdversarialNAS: Adversarial Neural Architecture Search for GANs
This work addresses the challenge of efficiently searching for optimal GAN architectures, which is a domain-specific problem for researchers in generative modeling, and it is incremental as it builds on existing NAS techniques.
The paper tackles the problem of automating architecture design for Generative Adversarial Networks (GANs) in unconditional image generation, resulting in a method that achieves state-of-the-art FID scores of 10.87 on CIFAR-10 and 26.98 on STL-10.
Neural Architecture Search (NAS) that aims to automate the procedure of architecture design has achieved promising results in many computer vision fields. In this paper, we propose an AdversarialNAS method specially tailored for Generative Adversarial Networks (GANs) to search for a superior generative model on the task of unconditional image generation. The AdversarialNAS is the first method that can search the architectures of generator and discriminator simultaneously in a differentiable manner. During searching, the designed adversarial search algorithm does not need to comput any extra metric to evaluate the performance of the searched architecture, and the search paradigm considers the relevance between the two network architectures and improves their mutual balance. Therefore, AdversarialNAS is very efficient and only takes 1 GPU day to search for a superior generative model in the proposed large search space ($10^{38}$). Experiments demonstrate the effectiveness and superiority of our method. The discovered generative model sets a new state-of-the-art FID score of $10.87$ and highly competitive Inception Score of $8.74$ on CIFAR-10. Its transferability is also proven by setting new state-of-the-art FID score of $26.98$ and Inception score of $9.63$ on STL-10. Code is at: \url{https://github.com/chengaopro/AdversarialNAS}.