Soft Label

Soft labels, representing class membership probabilities instead of definitive assignments, are increasingly used in machine learning to address inherent label uncertainty and improve model robustness. Current research focuses on developing methods to generate and effectively utilize soft labels within various model architectures, including deep neural networks and Siamese networks, often incorporating techniques like label smoothing, contrastive learning, and prototype-based approaches. This work is significant because it enhances model generalization, calibration, and performance, particularly in scenarios with limited data, noisy labels, or complex data distributions, impacting diverse applications from image classification and object detection to music genre classification and medical image analysis.