Soft Prediction

Soft prediction, using probabilistic outputs rather than hard classifications, is a growing area of machine learning research focused on improving model reliability and interpretability. Current work explores techniques like knowledge distillation, where a complex "teacher" model provides soft predictions to train a simpler "student" model, and methods to calibrate these soft predictions to better reflect true uncertainty. This research aims to enhance model performance, particularly in addressing misclassifications and improving the explainability of AI decisions, with applications ranging from embedded systems to large-scale image classification.