An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

This paper provides a brief analytical review of the current state‐of‐the‐art in relation to the
explainability of artificial intelligence in the context of recent advances in machine learning …

The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …

Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …

The availability of large-scale image captioning and visual question answering datasets has
contributed significantly to recent successes in vision-and-language pre-training. However …

Unsupervised localization and segmentation are long-standing computer vision challenges
that involve decomposing an image into semantically-meaningful segments without any …

Existing public face image datasets are strongly biased toward Caucasian faces, and other
races (eg, Latino) are significantly underrepresented. The models trained from such datasets …

The goal of domain generalization algorithms is to predict well on distributions different from
those seen during training. While a myriad of domain generalization algorithms exist …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …