Soft Target

Soft targets, representing probabilistic rather than definitive labels, are increasingly used to improve machine learning model performance across diverse applications. Current research focuses on refining soft target methodologies for regression tasks, extreme multi-label classification, and semi-supervised learning, often incorporating techniques like knowledge distillation and distributional modeling within architectures such as mixture of experts models. These advancements enhance model robustness and generalization, particularly in scenarios with noisy or ambiguous data, impacting fields ranging from security vulnerability assessment to molecular property prediction and speech recognition.