2020/06/11 by Masanari Kimura, Kimura, Masanari
Computer Science · Mathematics · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2006.06231
openalex publication_date 2020/06/11 · arxiv created 2021/06/18 · arxiv updated 2021/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine learning techniques are used in a wide range of domains. However, machine learning models often suffer from the problem of over-fitting. Many data augmentation methods have been proposed to tackle such a problem, and one of them is called mixup. Mixup is a recently proposed regularization procedure, which linearly interpolates a random pair of training examples. This regularization method works very well experimentally, but its theoretical guarantee is not adequately discussed. In this study, we aim to discover why mixup works well from the aspect of the statistical learning theory.