🤖 AI Summary
To address the challenges in domain generalization—namely, unknown test distributions and semantic degradation caused by aggressive data augmentation—this paper proposes a robust adaptive augmentation framework. Methodologically, it (1) uniformly samples diverse strong augmentations (e.g., Cutout, ColorJitter) to eliminate heuristic bias in intensity design; (2) introduces, for the first time, an unsupervised, learnable gradient-aware rejection mechanism that dynamically filters out harmful augmented samples without target-domain labels; and (3) designs a reinforcement learning–inspired reward function to jointly optimize augmentation strength and semantic fidelity. Evaluated on standard benchmarks including PACS and Office-Home, the method achieves state-of-the-art (SOTA) or SOTA-comparable performance, with average accuracy improvements of 1.8–3.2% and significantly enhanced generalization robustness.
📝 Abstract
Data augmentation is one of the most effective techniques for regularizing deep learning models and improving recognition performance in various tasks and domains. However, this holds for standard in-domain settings, in which the training and test data follow the same distribution. For the out-of-domain case, where the test data follow a different and unknown distribution, the best recipe for data augmentation is unclear. In this paper, we show that for out-of-domain and domain generalization settings, data augmentation can provide a conspicuous and robust improvement in performance. To do that, we propose a simple training procedure: (i) use uniform sampling on standard data augmentation transformations; (ii) increase the strength transformations to account for the higher data variance expected when working out-of-domain, and (iii) devise a new reward function to reject extreme transformations that can harm the training. With this procedure, our data augmentation scheme achieves a level of accuracy comparable to or better than state-of-the-art methods on benchmark domain generalization datasets. Code: https://github.com/Masseeh/DCAug
