Long Tailed Multi Label

Long-tailed multi-label classification tackles the challenge of accurately predicting multiple labels from data where some labels are far more frequent than others. Current research focuses on developing robust loss functions that address class imbalance, employing techniques like data augmentation and self-training to improve the representation of under-represented classes, and leveraging transformer-based architectures for effective feature fusion, particularly in multi-view settings like medical image analysis. This field is crucial for improving the performance of machine learning models in real-world applications where data is inherently imbalanced and multi-faceted, such as medical diagnosis and text categorization, leading to more accurate and reliable predictions for all classes.