DO-GAN: A double oracle framework for generative adversarial networks

• Main Authors: AUNG, Aye Phyu Phye, WANG, Xinrun, YU, Runsheng, AN, Bo, JAYAVELU, Senthilnath, LI, Xiaoli
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Deep learning architectures and techniques; Image and video synthesis and generation; Optimization methods; Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/9136; https://ink.library.smu.edu.sg/context/sis_research/article/10139/viewcontent/DO_GAN_CVPR_2022_av.pdf
• Institution: Singapore Management University

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...