Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models
Journal:
arXiv
Published Date:
Jun 2, 2025
Abstract
To increase the trustworthiness of deep neural networks, it is critical to
improve the understanding of how they make decisions. This paper introduces a
novel unsupervised concept-based model for image classification, named
Learnable Concept-Based Model (LCBM) which models concepts as random variables
within a Bernoulli latent space. Unlike traditional methods that either require
extensive human supervision or suffer from limited scalability, our approach
employs a reduced number of concepts without sacrificing performance. We
demonstrate that LCBM surpasses existing unsupervised concept-based models in
generalization capability and nearly matches the performance of black-box
models. The proposed concept representation enhances information retention and
aligns more closely with human understanding. A user study demonstrates the
discovered concepts are also more intuitive for humans to interpret. Finally,
despite the use of concept embeddings, we maintain model interpretability by
means of a local linear combination of concepts.
