AdversarialNAS: Adversarial Neural Architecture Search for GANs

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 proposed method leverages an adversarial searching mechanism to search for the architectures of generator and discriminator simultaneously in a differentiable manner. Therefore, the searching algorithm considers the relevance and balance between the two networks leading to search for a superior generative model. Besides, AdversarialNAS does not need any extra evaluation metric to evaluate the performance of the architecture in each searching iteration, which is very efficient and can take only 1 GPU day to search for an optimal network architecture in a 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. Our code will be released to facilitate the related academic and industrial study.