Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

Interpretable methods based on prototypical patches recognize various components in an
image in order to explain their reasoning to humans. However, existing prototype-based …

Existing prototypical-based models address the black-box nature of deep learning.
However, they are sub-optimal as they often assume separate prototypes for each class …

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

There is growing interest in concept-based models (CBMs) that combine high-performance
and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A …

We present ProtoConcepts, a method for interpretable image classification combining deep
learning and case-based reasoning using prototypical parts. Existing work in prototype …

Continual learning enables incremental learning of new tasks without forgetting those
previously learned, resulting in positive knowledge transfer that can enhance performance …

The need for interpretable models has fostered the development of self-explainable
classifiers. Prior approaches are either based on multi-stage optimization schemes …

Despite recent success of deep learning models in research settings, their application in
sensitive domains remains limited because of their opaque decision-making processes …