The rise in deep neural networks (DNNs) has led to increased interest in explaining their
predictions. While many methods for this exist, there is currently no consensus on how to …

The advancements in deep learning-based methods for visual perception tasks have seen
astounding growth in the last decade, with widespread adoption in a plethora of application …

Machine learning models need to provide contrastive explanations, since people often seek
to understand why a puzzling prediction occurred instead of some expected outcome …

Clinical diagnosis of the eye is performed over multifarious data modalities including scalar
clinical labels, vectorized biomarkers, two-dimensional fundus images, and three …

This article presents a novel positive and negative set selection strategy for contrastive
learning of medical images based on labels that can be extracted from clinical data. In the …

In this paper, we advocate for two stages in a neural network's decision making process. The
first is the existing feed-forward inference framework where patterns in given data are …

In active learning, acquisition functions define informativeness directly on the representation
position within the model manifold. However, for most machine learning models (in …

In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional
neural network (DCNN) image classifiers, called L-CAM-Fm and L-CAM-Img, are proposed …

In this paper, we visualize and quantify the predictive uncertainty of gradient-based post hoc
visual explanations for neural networks. Predictive uncertainty refers to the variability in the …

In this article, we present a leap-forward expansion to the study of explainability in neural
networks by considering explanations as answers to abstract reasoning-based questions …