论文标题
非政策加固学习，以进行高效有效的GAN架构搜索
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
论文作者
论文摘要
在本文中，我们介绍了一种新的强化学习（RL）的神经体系结构搜索（NAS）方法，以进行有效有效的生成对抗网络（GAN）体系结构搜索。关键想法是将GAN体系结构搜索问题作为Markov决策过程（MDP），用于平滑架构采样，该问题通过针对潜在的全球最佳体系结构来实现更有效的基于RL的搜索算法。为了提高效率，我们利用了一种非政策gan体系结构搜索算法，该算法有效利用了先前策略生成的样品。对两个标准基准数据集（即CIFAR-10和STL-10）进行评估表明，该提出的方法能够发现高度竞争性的架构，从而获得总体上更好的图像生成结果，并减少了计算负担：7 GPU小时。我们的代码可从https://github.com/yuantian013/e2gan获得。
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 https://github.com/Yuantian013/E2GAN.
