Augmentation Method

Data augmentation techniques artificially expand training datasets to improve the robustness and generalization of machine learning models, particularly when dealing with limited data or imbalanced classes. Current research focuses on developing automated and adaptive augmentation strategies, often incorporating techniques like generative models (e.g., diffusion models, GANs), in-context learning, and Bayesian optimization to optimize augmentation policies for specific tasks and datasets. These advancements are significantly impacting various fields, including medical image analysis, natural language processing, and computer vision, by enhancing model performance and enabling more reliable predictions in data-scarce scenarios.