Data Processing for Robustness
Summary
Data processing techniques play a crucial role in improving the robustness and generalization capabilities of deep learning models, particularly in image classification and reinforcement learning tasks. Two notable approaches in this area are AugMix and Reinforcement Learning with Augmented Data (RAD). AugMix is a simple data processing method that enhances model robustness and uncertainty estimates for image classifiers, helping them withstand unforeseen corruptions and data shifts. Similarly, RAD is a plug-and-play module that applies various data augmentations, such as random translate, crop, color jitter, and patch cutout, to improve the data-efficiency and generalization of reinforcement learning algorithms. Both techniques have demonstrated significant improvements in performance across challenging benchmarks, with RAD setting new state-of-the-art results in pixel-based and state-based control tasks. These approaches highlight the importance of data augmentation strategies in enhancing model robustness and adaptability to diverse and unforeseen scenarios.