Tail Class

Long-tailed learning addresses the challenge of training machine learning models on datasets where some classes have far more examples than others, leading to biased performance favoring the "head" classes while neglecting the "tail" classes. Current research focuses on improving classification accuracy for tail classes through techniques like data augmentation (e.g., mixup, generating synthetic tail class examples), loss function modifications (e.g., re-weighting, margin calibration), and model architectures designed to handle class imbalance (e.g., dual-branch networks, multi-expert models). This research is crucial for improving the reliability and fairness of machine learning systems in real-world applications, where imbalanced data is common in domains such as medical image analysis and anomaly detection.