In this paper, we introduce a new reinforcement learning (RL) based neural
architecture search (NAS) methodology for effective and efficient generative
adversarial network (GAN) architecture search. The key idea is to formulate the
GAN architecture search problem as a Markov decision process (MDP) for smoother
architecture sampling, which enables a more effective RL-based search algorithm
by targeting the potential global optimal architecture. To improve efficiency,
we exploit an off-policy GAN architecture search algorithm that makes efficient
use of the samples generated by previous policies. Evaluation on two standard
benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed
method is able to discover highly competitive architectures for generally
better image generation results with a considerably reduced computational
burden: 7 GPU hours. Our code is available at
this https URL

本文提出了基于强化学习的神经架构搜索方法，用于寻找生成对抗网络的最佳结构。结合马尔科夫决策过程和离线搜索算法能够提高搜索效率，该方法能够以较少的计算负担找到高竞争性的架构，获得更好的图像生成结果。