Temperature Scaling

Temperature scaling is a post-hoc calibration method used to improve the reliability of model predictions by adjusting the confidence scores outputted by machine learning models, particularly deep neural networks. Current research focuses on adapting temperature scaling to various contexts, including handling data heterogeneity in federated learning, improving calibration in specific regions of the probability space for decision-making, and addressing the challenges of out-of-distribution generalization and long-tailed data distributions. These advancements are significant because well-calibrated models are crucial for trustworthy AI systems across diverse applications, from medical image analysis and weather forecasting to materials science and natural language processing.