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: https://github.com/chengaopro/AdversarialNAS.

本文提出了一种专为生成对抗网络（GAN）设计的 AdversarialNAS 方法，它是第一种能够同时搜索生成器和鉴别器架构的方法，并且不需要计算任何额外指标以评估搜索架构性能，该方法在 CIFAR-10 上取得了新的最优状态下的 FID 分数 10.87 和高竞争的 Inception Score 8.74，并且也在 STL-10 上取得了新的最优状态下的 FID 分数 26.98 和 Inception Score 9.63。