A Survey on Mixup Augmentations and Beyond

Understand

As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable.

• Among existing augmentations, Mixup and relevant data-mixing methods that convexly combine selected samples and the corresponding labels are widely adopted because they yield high performances by generating data-dependent virtual data while easily migrating to various domains.
• This survey presents a comprehensive review of foundational mixup methods and their applications.
• We first elaborate on the training pipeline with mixup augmentations as a unified framework containing modules.