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.
@article{arxiv.2506.02092,
title = {Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models},
author = {Francesco De Santis and Philippe Bich and Gabriele Ciravegna and Pietro Barbiero and Danilo Giordano and Tania Cerquitelli},
journal= {arXiv preprint arXiv:2506.02092},
year = {2025}
}