Long Tailed Learning

Long-tailed learning addresses the challenge of training machine learning models on datasets where class frequencies are highly skewed, with a few dominant classes ("head") and many under-represented classes ("tail"). Current research focuses on improving the accuracy of predictions for tail classes through techniques like data augmentation, loss function modifications (e.g., incorporating contrastive learning or CVaR), and architectural innovations (e.g., mixtures of experts, decoupled classifiers). This field is crucial for real-world applications where data imbalance is common (e.g., medical image analysis, object detection), improving the reliability and fairness of machine learning systems across all classes.