Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2022
Abstract
In this paper, we propose a new approach to train Gen-erative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discrim-inator oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. Training GANs is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend the double oracle framework to GANs. We first generalize the players' strategies as the trained models of generator and discriminator from the best response or-acles. We then compute the meta-strategies using a linear program. For scalability of the framework where multi-ple generators and discriminator best responses are stored in the memory, we propose two solutions: 1) pruning the weakly-dominated players' strategies to keep the oracles from becoming intractable; 2) applying continual learning to retain the previous knowledge of the networks. We apply our framework to established GAN architectures such as vanilla GAN, Deep Convolutional GAN, Spectral Normalization GAN and Stacked GAN. Finally, we conduct experiments on MNIST, CIFAR-10 and CelebA datasets and show that DO-GAN variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective GAN architectures.
Keywords
Deep learning architectures and techniques, Image and video synthesis and generation, Optimization methods
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): New Orleans, June 18-24: Proceedings
First Page
11265
Last Page
11274
ISBN
9781665469463
Identifier
10.1109/CVPR52688.2022.01099
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
AUNG, Aye Phyu Phye; WANG, Xinrun; YU, Runsheng; AN, Bo; JAYAVELU, Senthilnath; and LI, Xiaoli.
DO-GAN: A double oracle framework for generative adversarial networks. (2022). 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): New Orleans, June 18-24: Proceedings. 11265-11274.
Available at: https://ink.library.smu.edu.sg/sis_research/9136
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/CVPR52688.2022.01099