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

本文介绍了一项有关 AutoGAN 的初步研究，这是将神经架构搜索算法引入生成对抗网络中的首个尝试。通过使用 RNN 控制器来引导搜索生成器构架的变化，实验结果验证了 AutoGAN 在无条件图像生成任务中的有效性，并与当前最先进的手工制作的 GANs 相比，取得了高竞争性的表现。

AutoGAN: 生成对抗网络的神经结构搜索