Balanced and Entropy-based Mixing for Enhancing Long-Tailed Semi-Supervised Learning
This paper introduces Balanced and Entropy-based Mix (BEM), a novel data mixing approach that re-balances both the class distribution of data quantity and uncertainty to enhance long-tailed semi-supervised learning.
Complementary Experts for Effective Long-Tailed Semi-Supervised Learning
To address the challenge of long-tailed semi-supervised learning, where labeled data exhibit imbalanced class distribution and unlabeled data follow an unknown distribution, the authors propose a novel method named ComPlementary Experts (CPE) that trains multiple experts to model various class distributions, each yielding high-quality pseudo-labels within one form of class distribution. They also introduce Classwise Batch Normalization to avoid performance degradation caused by feature distribution mismatch between head and non-head classes.
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition: A Probabilistic Framework and Empirical Study
This research paper introduces Continuous Contrastive Learning (CCL), a novel method for Long-Tailed Semi-Supervised Learning (LTSSL) that leverages a probabilistic framework to unify existing long-tail learning approaches and utilizes continuous pseudo-labels for improved representation learning in imbalanced datasets.
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