Augmentation Composition

Augmentation composition in machine learning focuses on optimizing the combination of data augmentation techniques to improve model performance and robustness. Current research explores adaptive and learned augmentation strategies, moving beyond fixed augmentation pipelines, often within contrastive learning frameworks or knowledge distillation. This research aims to enhance model generalization, particularly in challenging scenarios like out-of-domain data or occluded images, leading to more efficient and effective training processes. The resulting improvements in model accuracy and robustness have significant implications for various applications, including image classification, action recognition, and person re-identification.