  Data augmentation (DA) is widely employed to improve the generalization
performance of deep models. However, most existing DA methods use augmentation
operations with random magnitudes throughout training. While this fosters
diversity, it can also inevitably introduce uncontrolled variability in
augmented data, which may cause misalignment with the evolving training status
of the target models. Both theoretical and empirical findings suggest that this
misalignment increases the risks of underfitting and overfitting. To address
these limitations, we propose AdaAugment, an innovative and tuning-free
Adaptive Augmentation method that utilizes reinforcement learning to
dynamically adjust augmentation magnitudes for individual training samples
based on real-time feedback from the target network. Specifically, AdaAugment
features a dual-model architecture consisting of a policy network and a target
network, which are jointly optimized to effectively adapt augmentation
magnitudes. The policy network optimizes the variability within the augmented
data, while the target network utilizes the adaptively augmented samples for
training. Extensive experiments across benchmark datasets and deep
architectures demonstrate that AdaAugment consistently outperforms other
state-of-the-art DA methods in effectiveness while maintaining remarkable
efficiency.
