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

Understand

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.