Game Theory Meets Data Augmentation
Yuhan Kang, Samira Zare, Alex Tong Lin, Zhu Han, Stanley Osher, Hien Van Nguyen · IEEE Transactions on Artificial Intelligence · 2024
Data augmentation is a critical component in building modern deep-learning systems. In this paper, we proposeMFG Augment, a novel data augmentation method based on the Mean-Field-Game (MFG) theory, that can synthesize a sequence of data between every two images or features. The central idea is to consider every image as a distribution over its pixel or feature space. Using Mean-field Game theory, we can generate a time-continuous “path” from one distribution to another so that the points along the “path” are augmented images or features. Empirically, the experiment results on MNIST, CIFAR-10, and ImageNet demonstrate that the proposed technology has better generalization ability and higher classification accuracy as compared to several benchmark methods. More importantly, ourMFG Augmentimproves the test accuracy significantly when the dataset size is small. MFG Augment consistently shows better affinity and diversity scores, two important empirical metrics for evaluating the generalization of data augmentation techniques.