Multi Label

Multi-label classification tackles the problem of assigning multiple, non-exclusive labels to a single data instance, addressing the limitations of single-label approaches in many real-world scenarios. Current research focuses on improving model robustness against adversarial attacks and handling class imbalances, often employing deep neural networks (including CNNs and Transformers), autoencoders for data augmentation, and contrastive learning techniques. These advancements are crucial for applications ranging from image recognition and bioacoustic analysis to medical diagnosis and natural language processing, enabling more nuanced and accurate interpretations of complex data.