  Neural architecture search (NAS) has witnessed prevailing success in image
classification and (very recently) segmentation tasks. In this paper, we
present the first preliminary study on introducing the NAS algorithm to
generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and
GANs faces its unique challenges. We define the search space for the generator
architectural variations and use an RNN controller to guide the search, with
parameter sharing and dynamic-resetting to accelerate the process. Inception
score is adopted as the reward, and a multi-level search strategy is introduced
to perform NAS in a progressive way. Experiments validate the effectiveness of
AutoGAN on the task of unconditional image generation. Specifically, our
discovered architectures achieve highly competitive performance compared to
current state-of-the-art hand-crafted GANs, e.g., setting new state-of-the-art
FID scores of 12.42 on CIFAR-10, and 31.01 on STL-10, respectively. We also
conclude with a discussion of the current limitations and future potential of
AutoGAN. The code is available at https://github.com/TAMU-VITA/AutoGAN
