3 accepted papers
Selective Classification with Out-of-Distribution Detection (SCOD) is a general framework that combines the detection of incorrectly classified in-distribution samples and out-of-distribution samples. Previous solutions for SCOD heavily rely on the choice of Selective Classification (SC) and Out-of-
Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, conventional uncertainty measures such as Shannon entropy do not provide an effective way to infer the real uncertainty ass…
As the number of AI systems keeps growing, it is fundamental to implement and develop efficient control mechanisms to ensure the safe and proper functioning of machine learning (ML) systems. Reliable out-of-distribution (OOD) detection aims to detect test samples that are statistically far from the…