AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation
AROID automatically learns online data augmentation policies to improve adversarial training robustness. It uses a multi-head neural network to select optimal, instance-specific transformations like flips or crops during training. This method is novel because it automates augmentation design, reducing computational search costs while significantly improving model resilience against adversarial attacks.