Abstract Explainable Artificial Intelligence (XAI) is an emerging research field bringing
transparency to highly complex and opaque machine learning (ML) models. Despite the …

It is important to quantify the uncertainty of input samples, especially in mission-critical
domains such as autonomous driving and healthcare, where failure predictions on out-of …

Conventionally, AI models are thought to trade off explainability for lower accuracy. We
develop a training strategy that not only leads to a more explainable AI system for object …

To interpret deep networks, one main approach is to associate neurons with human-
understandable concepts. However, existing methods often ignore the inherent connections …

By highlighting important features that contribute to model prediction, visual saliency is used
as a natural form to interpret the working mechanism of deep neural networks. Numerous …

In state-of-the-art deep single-label classification models, the top-accuracy is usually
significantly higher than the top-1 accuracy. This is more evident in fine-grained datasets …

The recent advances in deep learning predominantly construct models in their internal
representations, and it is opaque to explain the rationale behind and decisions to human …

Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ
computer vision and machine learning for precise, rapid defect detection. However, their …

Deep neural networks have achieved great success in many real-world applications, yet it
remains unclear and difficult to explain their decision-making process to an end user. In this …

This paper presents an approach integrating explainable artificial intelligence (XAI)
techniques with adaptive learning to enhance energy consumption prediction models, with a …