Mixed Sample Data Augmentation

Mixed sample data augmentation (MSDA) is a technique that enhances the training of machine learning models, particularly deep neural networks, by creating synthetic training examples through the mixing of existing data points. Current research focuses on improving MSDA's effectiveness, exploring variations like CutMix and Mixup, and addressing challenges such as class-dependent performance and the impact on model interpretability. These advancements aim to improve model generalization, robustness, and efficiency, particularly in scenarios with limited labeled data, impacting various applications from image classification to neural machine translation. The ongoing investigation into optimal mixing strategies and the theoretical underpinnings of MSDA is driving progress in this active area of research.