Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks

• URL: http://arxiv.org/abs/2410.18705v2
• Date: Mon, 04 Nov 2024 19:02:32 GMT
• Title: Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks
• Authors: Alba Carballo-Castro, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt,
• Abstract summary: We create concept-enriched models that incorporate concept information into existing architectures. In particular, we propose Concept-Guided Diffusion Conditional, which can generate visual representations of concepts, and Concept-Guided Prototype Networks, which can create a concept prototype dataset and leverage it to perform interpretable concept prediction. These results open up new lines of research by exploiting pre-existing information in the quest for rendering machine learning more human-understandable.
• Score: 8.391254800873599
• License:
• Abstract: Concept-based machine learning methods have increasingly gained importance due to the growing interest in making neural networks interpretable. However, concept annotations are generally challenging to obtain, making it crucial to leverage all their prior knowledge. By creating concept-enriched models that incorporate concept information into existing architectures, we exploit their interpretable capabilities to the fullest extent. In particular, we propose Concept-Guided Conditional Diffusion, which can generate visual representations of concepts, and Concept-Guided Prototype Networks, which can create a concept prototype dataset and leverage it to perform interpretable concept prediction. These results open up new lines of research by exploiting pre-existing information in the quest for rendering machine learning more human-understandable.

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