CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

• URL: http://arxiv.org/abs/2102.09559v1
• Date: Thu, 18 Feb 2021 18:59:57 GMT
• Title: CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
• Authors: Chen Wei, Kihyuk Sohn, Clayton Mellina, Alan Yuille, Fan Yang
• Abstract summary: We propose Class-Rebalancing Self-Training (CReST) to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets.
• Score: 15.671523625324388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudo-labeled samples from an unlabeled set, where pseudo-labeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods.

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