Transforming gradient-based techniques into interpretable methods
Caroline Mazini Rodrigues Nicolas Boutry Laurent Najman
The explication of Convolutional Neural Networks (CNN) through xAI techniques
often poses challenges in interpretation. The inherent complexity of input
features, notably pixels extracted from images, engenders complex correlations.
Gradient-based methodologies, exemplified by Integrated Gradients (IG),
effectively demonstrate the significance of these features. Nevertheless, the
conversion of these explanations into images frequently yields considerable
noise. Presently, we introduce GAD (Gradient Artificial Distancing) as a
supportive framework for gradient-based techniques. Its primary objective is to
accentuate influential regions by establishing distinctions between classes.
The essence of GAD is to limit the scope of analysis during visualization and,
consequently reduce image noise. Empirical investigations involving occluded
images have demonstrated that the identified regions through this methodology
indeed play a pivotal role in facilitating class differentiation.