CHOQOLATE: Organizing Concept Bottleneck Latent Spaces with Choquet Integrals
Concept Bottleneck Models (CBMs) built on vision-language models such as CLIP represent a latent space as human-understandable concepts. These representations are unfaithful: related concepts are entangled, so individual scores do not reflect their intended meaning. We propose CHOQOLATE, an interpretable-by-design layer based on 2-additive Choquet integrals, which merges correlated concepts into compact nodes. Across four datasets, CHOQOLATE achieves a favorable accuracy-interpretability trade-off, with weight-sparse and semantically coherent nodes. A closed-form gradient derivation, backed by experiments, explains why Choquet layers drive this organization without explicit supervision. C...
| Authors | Rémi Kazmierczak, Johanne Cohen, Marianne Clausel |
|---|---|
| Primary category | cs.CV |
| Published | 29 September 2026 |
| Last updated at source | 7 October 2026 |
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