Long Tailed Semi Supervised Learning

Long-tailed semi-supervised learning (LTSSL) tackles the challenge of training machine learning models on datasets where labeled data are heavily skewed towards certain classes, while abundant unlabeled data are available. Current research focuses on developing methods that effectively leverage unlabeled data to mitigate the class imbalance problem, often employing techniques like dual-branch training, logit adjustments, and data mixing strategies to improve the representation and classification of under-represented classes. These advancements are crucial for real-world applications where obtaining sufficient labeled data for all classes is expensive or impractical, improving the robustness and generalizability of models in diverse domains.