Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks

TL;DR

This paper introduces concept-guided diffusion and prototype networks that enhance interpretability in neural networks by leveraging concept information, even with limited annotations, to generate visual representations and improve concept prediction.

Contribution

It proposes novel concept-guided models that incorporate concept knowledge into diffusion and prototype networks, advancing interpretability in machine learning.

Findings

Generated visual representations of concepts.

Created a concept prototype dataset.

Enhanced interpretable concept prediction.

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.