With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …

Purpose In the rapidly expanding field of artificial intelligence (AI) there is a wealth of
literature detailing the myriad applications of AI, particularly in the realm of deep learning …

Explainable AI aims to overcome the black-box nature of complex ML models like neural
networks by generating explanations for their predictions. Explanations often take the form of …

Interpreting complex deep networks, notably pre-trained vision-language models (VLMs), is
a formidable challenge. Current Class Activation Map (CAM) methods highlight regions …

Explainability is a pivotal factor in determining whether a deep learning model can be
authorized in critical applications. To enhance the explainability of models of end-to-end …

A coal gangue image recognition method based on complex conditions is proposed to
address the current issue of image-based coal gangue recognition being greatly affected by …

This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-
CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is …

Deep learning models have been successfully applied to lung nodule malignancy analysis
but lack interpretability, for example, in linking underlying nodule visual attributes with …

Understanding what deep network models capture in their learned representations is a
fundamental challenge in computer vision. We present a new methodology to understanding …

There is limited understanding of the information captured by deep spatiotemporal models in
their intermediate representations. For example, while evidence suggests that action …