Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph
Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid

TL;DR
This paper introduces a novel method combining LIME and Aleph to generate logic-based explanations for image classifiers, capturing relational concepts and spatial relations that traditional visual explanations cannot convey.
Contribution
The approach integrates LIME with Aleph to produce relational logic rules explaining classifier decisions, enabling richer, relation-aware explanations for visual data.
Findings
Successfully identified important relational constructs in images.
Generated explicit relational rules for complex concepts like towers.
Produced explanations that relate logic rules directly to image features.
Abstract
With the increasing number of deep learning applications, there is a growing demand for explanations. Visual explanations provide information about which parts of an image are relevant for a classifier's decision. However, highlighting of image parts (e.g., an eye) cannot capture the relevance of a specific feature value for a class (e.g., that the eye is wide open). Furthermore, highlighting cannot convey whether the classification depends on the mere presence of parts or on a specific spatial relation between them. Consequently, we present an approach that is capable of explaining a classifier's decision in terms of logic rules obtained by the Inductive Logic Programming system Aleph. The examples and the background knowledge needed for Aleph are based on the explanation generation method LIME. We demonstrate our approach with images of a blocksworld domain. First, we show that our…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsLocal Interpretable Model-Agnostic Explanations
