  Ensuring the trustworthiness and interpretability of machine learning models
is critical to their deployment in real-world applications. Feature attribution
methods have gained significant attention, which provide local explanations of
model predictions by attributing importance to individual input features. This
study examines the generalization of feature attributions across various deep
learning architectures, such as convolutional neural networks (CNNs) and vision
transformers. We aim to assess the feasibility of utilizing a feature
attribution method as a future detector and examine how these features can be
harmonized across multiple models employing distinct architectures but trained
on the same data distribution. By exploring this harmonization, we aim to
develop a more coherent and optimistic understanding of feature attributions,
enhancing the consistency of local explanations across diverse deep-learning
models. Our findings highlight the potential for harmonized feature attribution
methods to improve interpretability and foster trust in machine learning
applications, regardless of the underlying architecture.
