Topic: out-of-distribution samples

Uncertainty calibration, the process of aligning a model’s predictive confidence with the likelihood of correctness, is crucial for artificial intelligence systems. Despite recent advancements in deep learning, conventional models often struggle to calibrate confidence in a manner similar to human judgment (Guo et al., 2017). Specifically, while increased model capacity facilitates accurate classification, it frequently undermines confidence calibration. For instance, these models tend to exhibit overconfidence when confronted with unknown outlier samples, even in the absence of relevant knowl