Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2025
Abstract
In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where we deploy a double-oracle framework using best response oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. The same concept can be applied to AT with attacker and classifier as players. Training these models is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as training algorithms for both GAN and AT have a large-scale strategy space. Extending our preliminary model DO-GAN, we propose the methods to apply the double oracle framework concept to Adversarial Neural Architecture Search (NAS for GAN) and Adversarial Training (NAS for AT) algorithms. We first generalize the players’ strategies as the trained models of generator and discriminator from the best response oracles. We then compute the meta-strategies using a linear program. For scalability of the framework where multiple network models of best responses are stored in the memory, we prune the weakly-dominated players’ strategies to keep the oracles from becoming intractable. Finally, we conduct experiments on MNIST, CIFAR-10 and TinyImageNet for DONAS-GAN. We also evaluate the robustness under FGSM and PGD attacks on CIFAR-10, SVHN and TinyImageNet for DONAS-AT. We show that all our variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective base architectures.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
IEEE Transactions on Image Processing
Volume
34
First Page
2463
Last Page
2472
ISSN
1057-7149
Identifier
10.1109/TIP.2025.3558420
Publisher
Institute of Electrical and Electronics Engineers
Citation
AUNG, Aye Phyu Phyu; WANG, Xinrun; WANG, Ruiyu; CHAN, Hau; AN, Bo; LI, Xiaoli; and SENTHILNATH, J..
Double oracle neural architecture search for game theoretic deep learning models. (2025). IEEE Transactions on Image Processing. 34, 2463-2472.
Available at: https://ink.library.smu.edu.sg/sis_research/10620
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/TIP.2025.3558420