CHOQOLATE: Organizing Concept Bottleneck Latent Spaces with Choquet Integrals

arXiv:2609.37786 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-10-07

Code: https://github.com/yossigandelsman/clip_text_span

Terminology

• Accuracy — 72× in this paper · explained in 1 episode
• Interpretability — 14× in this paper · explained in 15 episodes
• Concept Bottleneck Model — 12× in this paper
• Latent Space — 12× in this paper · explained in 7 episodes
• Sparsity — 10× in this paper · explained in 4 episodes
• Redundancy — 9× in this paper · explained in 2 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Shapley values — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Regularization — 4× in this paper · explained in 3 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Post-hoc Concept Removal — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• In European Conference on Computer Vision — 1× in this paper
• M Choquet Integral — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• SHAP values — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Hoeffding Concept Bottleneck Models with Applications to Overhead Images
• Debiasing Vision-Language Models via Biased Prompts
• If Concept Bottlenecks are the Question, are Foundation Models the Answer?
• Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
• The Platonic Representation Hypothesis
• Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
• Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
• Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
• Common-Sense Bias Modeling for Classification Tasks
• Places: An Image Database for Deep Scene Understanding

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