Data Augmentation Policy

Data augmentation policy research focuses on optimizing how data is artificially modified during training to improve the robustness and generalization of machine learning models. Current research explores automated methods, such as AutoAugment and its variants, which learn optimal augmentation strategies, often leveraging Bayesian optimization or gradient-based approaches, and adapting policies based on label information or model size. This field is crucial for improving model performance, particularly in data-scarce scenarios and for resource-constrained applications, and is actively advancing the efficiency and effectiveness of deep learning across various domains.