Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting
and enhance their generalization and performance, various methods have been suggested …

Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world
intelligent systems. Despite the emergence of an increasing number of OOD detection …

Abstract Machine learning models have been found to learn shortcuts---unintended decision
rules that are unable to generalize---undermining models' reliability. Previous works address …

Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental
requirement in high-stakes applications. Unfortunately, modern deep neural networks are …

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

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 …

It is now well known that neural networks can be wrong with high confidence in their
predictions, leading to poor calibration. The most common post-hoc approach to …

Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-
world autonomous systems. However, most successful approaches are computationally …

Graph neural networks (GNNs) have been demonstrated to perform well in graph
representation learning, but always lacking of generalization capability when tackling out-of …