As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

Explanation supervision is a technique in which the model is guided by human-generated
explanations during training. This technique aims to improve the predictability of the model …

Abstract The Segment Anything Model (SAM) is a powerful foundation model that introduced
revolutionary advancements in natural image segmentation. However, its performance …

Explanation supervision aims to enhance deep learning models by integrating additional
signals to guide the generation of model explanations, showcasing notable improvements in …

Explanation (attention)-guided learning is a method that enhances a model's predictive
power by incorporating human understanding during the training phase. While attention …

Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for
addressing their" black box" nature, particularly in tasks like image classification. Traditional …

The Segment Anything Model (SAM) is a powerful foundation model that introduced
revolutionary advancements in natural image segmentation. However, its performance …

This work introduces a novel class of Data Augmentation (DA) techniques in the context of
graph learning. In general, DA refers to techniques that enlarge the training set using label …