Data Augmentation Algorithm

Data augmentation algorithms aim to artificially expand training datasets by generating modified versions of existing data, improving the robustness and generalization of machine learning models, particularly when data is scarce or imbalanced. Current research focuses on automating the augmentation process, developing algorithms tailored to specific data types (e.g., graphs, time series, images), and optimizing augmentation strategies for self-supervised learning and imbalanced datasets. These advancements are significant because they enhance model performance across various applications, reducing the need for extensive data collection and improving the efficiency of machine learning workflows.