arXiv AI
Auditing Where Concept Bottleneck Accuracy Comes From on CUB
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On the Faithfulness of Post-Hoc Concept Bottleneck Models
arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.
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Assessing Reliability of Symbol Detection in Concept Bottleneck Models
arXiv:2606. 16535v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) are a relevant tool for explainable Artificial Intelligence because they make their predictions through human-interpretable symbols.
arXiv Machine Learning
Jun 4
Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
arXiv:2606. 04364v1 Announce Type: cross Abstract: Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable.
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How to make effective use of domain experts for image classification?
arXiv:2609.17749v1 Announce Type: new Abstract: A lot of expectations have been put for years on integrating domain expert knowledge in image classification models. Several approaches have been explo...
arXiv Machine Learning
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Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.
arXiv Computer Vision
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Aperture: Training-Free Multiscale Concept Bottlenecks for Remote Sensing
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