Given imbalanced data, it is hard to train a good classifier using deep learning because of the poor generalization of minority classes. Traditionally, the well-known synthetic minority oversampling technique (SMOTE) for data augmentation, a data mining approach for imbalanced learning, has been used to improve this generalization. However, it is unclear whether SMOTE also benefits deep learning. In this work, we study why the original SMOTE is insufficient for deep learning, and enhance SMOTE using soft labels. Connecting the resulting soft SMOTE with Mixup, a modern data augmentation technique, leads to a unified framework that puts traditional and modern data augmentation techniques under the same umbrella. A careful study within this framework shows that Mixup improves generalization by implicitly achieving uneven margins between majority and minority classes. We then propose a novel margin-aware Mixup technique that more explicitly achieves uneven margins. Extensive experimental results demonstrate that our proposed technique yields state-of-the-art performance on deep imbalanced classification while achieving superior performance on extremely imbalanced data. The code is open-sourced in our developed package https://github.com/ntucllab/imbalanced-DL to foster future research in this direction.

翻译：针对不平衡数据，由于少数类泛化能力差，使用深度学习训练优质分类器十分困难。传统上，用于不平衡学习的数据挖掘方法——合成少数类过采样技术（SMOTE）作为数据增强手段被广泛采用以改善泛化性能。然而，SMOTE是否同样适用于深度学习尚不明确。本文研究了原始SMOTE在深度学习中失效的原因，并引入软标签对其进行改进。将改进后的软SMOTE与现代化数据增强技术Mixup相结合，构建了一个统一框架，将传统与现代数据增强技术纳入同一体系。该框架的深入研究表明，Mixup通过隐式实现多数类与少数类间的不均匀间隔来提升泛化性能。我们进一步提出一种显式实现不均匀间隔的间隔感知Mixup技术。大量实验结果表明，所提方法在深度不平衡分类任务上达到最优性能，尤其在极端不平衡数据上表现卓越。相关代码已在开源包https://github.com/ntucllab/imbalanced-DL中发布，以促进该方向的后续研究。