Class Mixup

Mixup is a data augmentation technique that generates synthetic training samples by linearly interpolating pairs of data points and their corresponding labels. Current research focuses on improving mixup's effectiveness through variations like multi-mix (generating multiple interpolations), intra-class mixup (focusing on samples within the same class), and decoupled mixup (separating discriminative and noise-prone regions). These advancements aim to enhance model generalization, robustness, and calibration across various tasks, including image classification, object detection, and even reinforcement learning, ultimately leading to more accurate and reliable machine learning models.