From Game Theory to Visual Recognition: Advancing DNN Robustness

Abstract

In many real-world applications of Deep Neural Networks (DNNs) in visual recognition, data augmentation stands out as a premier tool for enhancing model robustness. Stemming from the understanding of the common mechanisms of data augmentation methods, we introduce the mask-based "data augmentation boost" (DaBoost) method, a strategic approach that exploits the control of game interaction strength. Our empirical results are telling: DaBoost not only consistently surpasses the state-of-the-art PixMix method but also achieves impressive robustness metrics, with a vanilla WideResNet registering a mere 6.5% mCE and a 2.3% RMS calibration error on CIFAR-10 data. An intriguing observation from our study is the Long-Rope Effect. We discerned that penalizing high-order interactions inadvertently leads to a boost in mid-order interactions, mirroring patterns inherent to human cognitive processes. This interplay hints at the potential avenues for optimizing DNNs’ performance further.