The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

To deal with the domain shift between training and test samples, current methods have
primarily focused on learning generalizable features during training and ignore the …

In spite of the dominant performances of deep neural networks, recent works have shown
that they are poorly calibrated, resulting in over-confident predictions. Miscalibration can be …

Given the importance of getting calibrated predictions and reliable uncertainty estimations,
various post-hoc calibration methods have been developed for neural networks on standard …

Abstract Deep Neural Networks (DNNs) are known to make overconfident mistakes, which
makes their use problematic in safety-critical applications. State-of-the-art (SOTA) calibration …

Reliable application of machine learning-based decision systems in the wild is one of the
major challenges currently investigated by the field. A large portion of established …

We study the problem of semantic segmentation calibration. Lots of solutions have been
proposed to approach model miscalibration of confidence in image classification. However …

Albeit revealing impressive predictive performance for several computer vision tasks, deep
neural networks (DNNs) are prone to making overconfident predictions. This limits the …

With model trustworthiness being crucial for sensitive real-world applications, practitioners
are putting more and more focus on improving the uncertainty calibration of deep neural …

Network calibration aims to accurately estimate the level of confidences, which is particularly
important for employing deep neural networks in real-world systems. Recent approaches …