ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

• URL: http://arxiv.org/abs/2110.10368v1
• Date: Wed, 20 Oct 2021 04:07:48 GMT
• Title: ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning
• Authors: Hyuck Lee, Seungjae Shin, Heeyoung Kim
• Abstract summary: Existing semi-supervised learning (SSL) algorithms assume class-balanced datasets. We propose a scalable class-imbalanced SSL algorithm that can effectively use unlabeled data. The proposed algorithm achieves state-of-the-art performance in various class-imbalanced SSL experiments using four benchmark datasets.
• Score: 6.866717993664787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing semi-supervised learning (SSL) algorithms typically assume class-balanced datasets, although the class distributions of many real-world datasets are imbalanced. In general, classifiers trained on a class-imbalanced dataset are biased toward the majority classes. This issue becomes more problematic for SSL algorithms because they utilize the biased prediction of unlabeled data for training. However, traditional class-imbalanced learning techniques, which are designed for labeled data, cannot be readily combined with SSL algorithms. We propose a scalable class-imbalanced SSL algorithm that can effectively use unlabeled data, while mitigating class imbalance by introducing an auxiliary balanced classifier (ABC) of a single layer, which is attached to a representation layer of an existing SSL algorithm. The ABC is trained with a class-balanced loss of a minibatch, while using high-quality representations learned from all data points in the minibatch using the backbone SSL algorithm to avoid overfitting and information loss.Moreover, we use consistency regularization, a recent SSL technique for utilizing unlabeled data in a modified way, to train the ABC to be balanced among the classes by selecting unlabeled data with the same probability for each class. The proposed algorithm achieves state-of-the-art performance in various class-imbalanced SSL experiments using four benchmark datasets.

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