11 citations · 14 across the 11 of their papers we have counts for
5 papers · 1 filter
Multi-Depth Concept Extraction for Post-Hoc Vision Encoder Explanation
Ahcène Boubekki, Samuel G. Fadel, Sebastian Mair
Explainable AI methods for vision models aim to identify the parts of the input that are important for the final prediction and subsequently relate these regions to human-understan…
Explainable AI needs formalization
Stefan Haufe, Rick Wilming, Benedict Clark +4
The field of "explainable artificial intelligence" (XAI) seemingly addresses the desire that decisions of machine learning systems should be human-understandable. However, in its c…
Leveraging Activations for Superpixel Explanations
Ahcène Boubekki, Samuel G. Fadel, Sebastian Mair
Saliency methods have become standard in the explanation toolkit of deep neural networks. Recent developments specific to image classifiers have investigated region-based explanati…
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
Benedict Clark, Rick Wilming, Artur Dox +11
The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalis…
Pantypes: Diverse Representatives for Self-Explainable Models
Rune Kjærsgaard, Ahcène Boubekki, Line Clemmensen
Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems. These classifiers are designed to incorporate high transparency in t…