Hierarchical, Interpretable, Label-Free Concept Bottleneck Model

• URL: http://arxiv.org/abs/2604.02468v1
• Date: Thu, 02 Apr 2026 19:02:59 GMT
• Title: Hierarchical, Interpretable, Label-Free Concept Bottleneck Model
• Abstract summary: CBMs introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts.<n>We propose HIL-CBM, a hierarchical framework to enhance interpretability by more closely mirroring the human cognitive process.<n>HIL-CBM aligns the abstraction level of concept-based explanations with that of model predictions, progressing from abstract to concrete.
• Score: 26.8734811438552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Concept Bottleneck Models (CBMs) introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts. However, unlike humans, who identify objects at different levels of abstraction using both general and specific features, existing CBMs operate at a single semantic level in both concept and label space. We propose HIL-CBM, a Hierarchical Interpretable Label-Free Concept Bottleneck Model that extends CBMs into a hierarchical framework to enhance interpretability by more closely mirroring the human cognitive process. HIL-CBM enables classification and explanation across multiple semantic levels without requiring relational concept annotations. HIL-CBM aligns the abstraction level of concept-based explanations with that of model predictions, progressing from abstract to concrete. This is achieved by (i) introducing a gradient-based visual consistency loss that encourages abstraction layers to focus on similar spatial regions, and (ii) training dual classification heads, each operating on feature concepts at different abstraction levels. Experiments on benchmark datasets demonstrate that HIL-CBM outperforms state-of-the-art sparse CBMs in classification accuracy. Human evaluations further show that HIL-CBM provides more interpretable and accurate explanations, while maintaining a hierarchical and label-free approach to feature concepts.

Related papers

• Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks [4.944427102119257]
  Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space.<n>Recent approaches leverage vision-language models to generate concept embeddings, reducing the need for manual concept annotations.<n>We propose Concept Flow Models (CFMs), which replace the flat bottleneck with a hierarchical, concept-driven decision tree.
  arXiv  Detail & Related papers  (2026-06-17T18:27:17Z)
• ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning [134.61875033137514]
  Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding.<n>We formalize concept segmentation through a three-level taxonomy consisting of context-independent (CI), context-dependent (CD), and context-reasoning (CR) concepts.<n>We propose ConceptSeg-R1, a unified framework that reformulates concept segmentation as rule-induced concept grounding.
  arXiv  Detail & Related papers  (2026-05-19T18:35:38Z)
• Digging Deeper: Learning Multi-Level Concept Hierarchies [23.16720677779406]
  Concept-based models typically rely on exhaustive annotations to explain predictions with human-understandable concepts.<n>We introduce Multi-Level Concept Splitting (MLCS), which discovers multi-level concept hierarchies from only top-level supervision.<n>We also present Deep-HiCEMs, an architecture that represents these discovered hierarchies and enables interventions at multiple levels of abstraction.
  arXiv  Detail & Related papers  (2026-03-10T12:48:20Z)
• A Geometric Unification of Concept Learning with Concept Cones [58.70836885177496]
  Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs) and Sparse Autoencoders (SAEs)<n>We show that both paradigms instantiate the same geometric structure.<n>CBMs provide human-defined reference geometries, while SAEs can be evaluated by how well their learned cones approximate or contain those of CBMs.
  arXiv  Detail & Related papers  (2025-12-08T09:51:46Z)
• Towards more holistic interpretability: A lightweight disentangled Concept Bottleneck Model [5.700536552863068]
  Concept Bottleneck Models (CBMs) enhance interpretability by predicting human-understandable concepts as intermediate representations.<n>We propose a lightweight Disentangled Concept Bottleneck Model (LDCBM) that automatically groups visual features into semantically meaningful components.<n> Experiments on three diverse datasets demonstrate that LDCBM achieves higher concept and class accuracy, outperforming previous CBMs in both interpretability and classification performance.
  arXiv  Detail & Related papers  (2025-10-17T15:59:30Z)
• Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts [79.18608192761512]
  Self-Explainable Models (SEMs) rely on Prototypical Concept Learning (PCL) to enable their visual recognition processes more interpretable.<n>We propose a Few-Shot Prototypical Concept Classification framework that mitigates two key challenges under low-data regimes: parametric imbalance and representation misalignment.<n>Our approach consistently outperforms existing SEMs by a notable margin, with 4.2%-8.7% relative gains in 5-way 5-shot classification.
  arXiv  Detail & Related papers  (2025-06-05T06:39:43Z)
• Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability [54.420663939897686]
  We propose the Attribute-formed Language Bottleneck Model (ALBM) to achieve interpretable image recognition.<n>ALBM organizes concepts in the attribute-formed class-specific space, where concepts are descriptions of specific attributes for specific classes.<n>To further improve interpretability, we propose Visual Attribute Prompt Learning (VAPL) to extract visual features on fine-grained attributes.
  arXiv  Detail & Related papers  (2025-03-26T07:59:04Z)
• Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
  Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks. We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
  arXiv  Detail & Related papers  (2024-07-19T17:50:11Z)
• MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes [24.28807025839685]
  We argue that explanations lacking insights into the decision processes of low and mid-level features are neither fully faithful nor useful. We propose a novel paradigm that learns and aligns multi-level concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss.
  arXiv  Detail & Related papers  (2024-04-13T11:13:56Z)
• Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning [17.982131928413096]
  Concept Bottleneck Models (CBMs) aim to deliver interpretable and interventionable predictions by bridging features and labels with human-understandable concepts. CBMs suffer from information leakage, where unintended information beyond the concepts are leaked to the subsequent label prediction. This paper proposes a new paradigm of CBMs, namely SupCBM, which achieves label predication via predicted concepts and a deliberately-designed intervention matrix.
  arXiv  Detail & Related papers  (2024-02-03T03:50:58Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.