Beyond Accuracy: Reliability and Uncertainty Estimation in Convolutional Neural Networks

ArXi:2603.10731v1 Announce Type: new Deep neural networks (DNNs) have become integral to a wide range of scientific and practical applications due to their flexibility and strong predictive performance. Despite their accuracy, however, DNNs frequently exhibit poor calibration, often assigning overly confident probabilities to incorrect predictions. This limitation underscores the growing need for integrated mechanisms that provide reliable uncertainty estimation.