This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on
an unlabeled target domain by utilizing the supervised model trained on a labeled source …

Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

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

Reliable confidence estimation for the predictions is important in many safety-critical
applications. However, modern deep neural networks are often overconfident for their …

Pedestrian Detection is the most critical module of an Autonomous Driving system. Although
a camera is commonly used for this purpose, its quality degrades severely in low-light night …

Undesired toxicity is a major hindrance to drug discovery and largely responsible for high
attrition rates in early stages. This calls for new, reliable, and interpretable molecular …

Traditional pattern recognition models usually assume a fixed and identical number of
classes during both training and inference stages. In this paper, we study an interesting but …