Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2020
Abstract
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.
Keywords
Generative adversarial networks; Markov decision process; Neural architecture search; Off-policy; Reinforcement learning
Discipline
Artificial Intelligence and Robotics | Systems Architecture
Research Areas
Data Science and Engineering
Publication
Proceedings of the 16th European Conference, Glasgow, UK, August 23–28
Volume
12352
First Page
175
Last Page
192
ISBN
9783030585709
Identifier
10.1007/978-3-030-58571-6_11
Publisher
Springer
City or Country
Glasgow
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.