This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation

• URL: http://arxiv.org/abs/2108.12204v1
• Date: Fri, 27 Aug 2021 09:55:53 GMT
• Title: This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
• Authors: Srishti Gautam, Marina M.-C. H\"ohne, Stine Hansen, Robert Jenssen and Michael Kampffmeyer
• Abstract summary: We present a case study of the self-explaining network, ProtoPNet, in the presence of a spectrum of artifacts. We introduce a novel method for generating more precise model-aware explanations. In order to obtain a clean dataset, we propose to use multi-view clustering strategies for segregating the artifact images.
• Score: 17.485732906337507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the understanding and traceability of the underlying decision-making strategies. As a remedy, many post-hoc explanation and self-explanatory methods have been developed to interpret the models' behavior. These methods, in addition, enable the identification of artifacts that can be learned by the model as class-relevant features. In this work, we provide a detailed case study of the self-explaining network, ProtoPNet, in the presence of a spectrum of artifacts. Accordingly, we identify the main drawbacks of ProtoPNet, especially, its coarse and spatially imprecise explanations. We address these limitations by introducing Prototypical Relevance Propagation (PRP), a novel method for generating more precise model-aware explanations. Furthermore, in order to obtain a clean dataset, we propose to use multi-view clustering strategies for segregating the artifact images using the PRP explanations, thereby suppressing the potential artifact learning in the models.

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