Mixup Method

Mixup is a data augmentation technique that improves the generalization of deep learning models by creating synthetic training samples through linear interpolation of input data and their corresponding labels. Current research focuses on refining mixup strategies, including exploring different interpolation methods (e.g., in feature space, using manifold approximations, or incorporating attention mechanisms), and adapting mixup for various tasks and data types (e.g., images, graphs, text, time series). These advancements enhance model robustness, particularly in scenarios with limited data, noisy labels, or domain shifts, leading to improved performance across diverse applications like image classification, medical image analysis, and fraud detection.