Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …

Abstract Deep Neural Networks (DNNs) demonstrated remarkable capabilities in learning
complex hierarchical data representations, but the nature of these representations remains …

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 …

Deep Neural Networks are prone to learning spurious correlations embedded in the training
data, leading to potentially biased predictions. This poses risks when deploying these …

Deep Neural Networks (DNNs) are capable of learning complex and versatile
representations, however, the semantic nature of the learned concepts remains unknown. A …

Robustness has become an important consideration in deep learning. With the help of
explainable AI, mismatches between an explained model's decision strategy and the user's …

In the field of Computer Vision (CV), the degree to which two objects, eg two classes, share
a common conceptual meaning, known as semantic similarity, is closely linked to the visual …

Explainable Artificial Intelligence (XAI) aims to make learning machines less opaque, and
offers researchers and practitioners various tools to reveal the decision-making strategies of …

A crucial aspect of understanding the complex nature of Deep Neural Networks (DNNs) is
the ability to explain learned concepts within their latent representations. While various …

The utilization of pre-trained networks, especially those trained on ImageNet, has become a
common practice in Computer Vision. However, prior research has indicated that a …