Label Distribution

Label distribution learning addresses the challenge of predicting the probability distribution over multiple labels for a single data point, moving beyond traditional single-label classification. Current research focuses on handling skewed or imbalanced label distributions, particularly within federated learning settings, employing techniques like dataset distillation, label masking, and various model adaptations (e.g., logit adjustments, feature augmentation). These advancements are crucial for improving the robustness and accuracy of machine learning models in real-world applications where data is often heterogeneous and noisy, impacting fields like medical image analysis, autonomous driving, and natural language processing.