CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning

• URL: http://arxiv.org/abs/2112.04564v1
• Date: Wed, 8 Dec 2021 20:13:13 GMT
• Title: CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning
• Authors: Yue Fan and Dengxin Dai and Bernt Schiele
• Abstract summary: We propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL. To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE) for classifier learning. In experiments, we show that our approach outperforms other methods over a large range of shifted distributions.
• Score: 98.89092930354273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL. To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE) for classifier learning. Furthermore, the current evaluation protocol for imbalanced SSL focuses only on balanced test sets, which has limited practicality in real-world scenarios. Therefore, we further conduct a comprehensive evaluation under various shifted test distributions. In experiments, we show that our approach outperforms other methods over a large range of shifted distributions, achieving state-of-the-art performance on benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our code will be made publicly available.

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