Interpretable Generative Models through Post-hoc Concept Bottlenecks
Fuente:
arXiv
Saved in:
| Main Authors: | Kulkarni, Akshay, Yan, Ge, Sun, Chung-En, Oikarinen, Tuomas, Weng, Tsui-Wei |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability
by: Oikarinen, Tuomas, et al.
Published: (2025)
by: Oikarinen, Tuomas, et al.
Published: (2025)
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
by: Javadi, Amirhosein, et al.
Published: (2026)
by: Javadi, Amirhosein, et al.
Published: (2026)
Interpreting Neurons in Deep Vision Networks with Language Models
by: Bai, Nicholas, et al.
Published: (2024)
by: Bai, Nicholas, et al.
Published: (2024)
Linear Explanations for Individual Neurons
by: Oikarinen, Tuomas, et al.
Published: (2024)
by: Oikarinen, Tuomas, et al.
Published: (2024)
Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
by: Kulkarni, Akshay, et al.
Published: (2025)
by: Kulkarni, Akshay, et al.
Published: (2025)
Concept Bottleneck Large Language Models
by: Sun, Chung-En, et al.
Published: (2024)
by: Sun, Chung-En, et al.
Published: (2024)
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
by: Srivastava, Divyansh, et al.
Published: (2024)
by: Srivastava, Divyansh, et al.
Published: (2024)
Interpretability-Guided Test-Time Adversarial Defense
by: Kulkarni, Akshay, et al.
Published: (2024)
by: Kulkarni, Akshay, et al.
Published: (2024)
Crafting Large Language Models for Enhanced Interpretability
by: Sun, Chung-En, et al.
Published: (2024)
by: Sun, Chung-En, et al.
Published: (2024)
Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
by: Yan, Ge, et al.
Published: (2025)
by: Yan, Ge, et al.
Published: (2025)
RAT: Boosting Misclassification Detection Ability without Extra Data
by: Yan, Ge, et al.
Published: (2025)
by: Yan, Ge, et al.
Published: (2025)
Evaluating Neuron Explanations: A Unified Framework with Sanity Checks
by: Oikarinen, Tuomas, et al.
Published: (2025)
by: Oikarinen, Tuomas, et al.
Published: (2025)
Provably Robust Conformal Prediction with Improved Efficiency
by: Yan, Ge, et al.
Published: (2024)
by: Yan, Ge, et al.
Published: (2024)
Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
by: Tong, Schrasing, et al.
Published: (2026)
by: Tong, Schrasing, et al.
Published: (2026)
Debugging Concept Bottleneck Models through Removal and Retraining
by: Enouen, Eric, et al.
Published: (2025)
by: Enouen, Eric, et al.
Published: (2025)
Concept Bottleneck Models Without Predefined Concepts
by: Schrodi, Simon, et al.
Published: (2024)
by: Schrodi, Simon, et al.
Published: (2024)
Hyperbolic Concept Bottleneck Models
by: Uyterlinde, Daniel, et al.
Published: (2026)
by: Uyterlinde, Daniel, et al.
Published: (2026)
Flexible Concept Bottleneck Model
by: Du, Xingbo, et al.
Published: (2025)
by: Du, Xingbo, et al.
Published: (2025)
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
by: Xu, Xinyue, et al.
Published: (2024)
by: Xu, Xinyue, et al.
Published: (2024)
ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models
by: Sun, Chung-En, et al.
Published: (2025)
by: Sun, Chung-En, et al.
Published: (2025)
Post-hoc Probabilistic Vision-Language Models
by: Baumann, Anton, et al.
Published: (2024)
by: Baumann, Anton, et al.
Published: (2024)
Towards Faithful Multimodal Concept Bottleneck Models
by: Moreau, Pierre, et al.
Published: (2026)
by: Moreau, Pierre, et al.
Published: (2026)
Concept-Centric Token Interpretation for Vector-Quantized Generative Models
by: Yang, Tianze, et al.
Published: (2025)
by: Yang, Tianze, et al.
Published: (2025)
Post-hoc Self-explanation of CNNs
by: Boubekki, Ahcène, et al.
Published: (2026)
by: Boubekki, Ahcène, et al.
Published: (2026)
Editable Concept Bottleneck Models
by: Hu, Lijie, et al.
Published: (2024)
by: Hu, Lijie, et al.
Published: (2024)
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification
by: Bianchi, Matteo, et al.
Published: (2024)
by: Bianchi, Matteo, et al.
Published: (2024)
Zero-shot Concept Bottleneck Models
by: Yamaguchi, Shin'ya, et al.
Published: (2025)
by: Yamaguchi, Shin'ya, et al.
Published: (2025)
Process-Guided Concept Bottleneck Model
by: Asiyabi, Reza M., et al.
Published: (2026)
by: Asiyabi, Reza M., et al.
Published: (2026)
Semi-supervised Concept Bottleneck Models
by: Hu, Lijie, et al.
Published: (2024)
by: Hu, Lijie, et al.
Published: (2024)
LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images
by: Flora, James, et al.
Published: (2026)
by: Flora, James, et al.
Published: (2026)
Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations
by: Gong, Shizhan, et al.
Published: (2026)
by: Gong, Shizhan, et al.
Published: (2026)
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
by: De Santis, Antonio, et al.
Published: (2024)
by: De Santis, Antonio, et al.
Published: (2024)
Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset
by: Nápoles, Gonzalo, et al.
Published: (2026)
by: Nápoles, Gonzalo, et al.
Published: (2026)
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
by: Singhi, Nishad, et al.
Published: (2024)
by: Singhi, Nishad, et al.
Published: (2024)
Post-hoc Selective Classification for Reliable Synthetic Image Detection
by: Zheng, Kaixiang, et al.
Published: (2026)
by: Zheng, Kaixiang, et al.
Published: (2026)
Innovative Silicosis and Pneumonia Classification: Leveraging Graph Transformer Post-hoc Modeling and Ensemble Techniques
by: Bui, Bao Q., et al.
Published: (2024)
by: Bui, Bao Q., et al.
Published: (2024)
Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
by: Mossina, Luca, et al.
Published: (2024)
by: Mossina, Luca, et al.
Published: (2024)
Mitigating Spurious Background Bias in Multimedia Recognition with Disentangled Concept Bottlenecks
by: Huang, Gaoxiang, et al.
Published: (2025)
by: Huang, Gaoxiang, et al.
Published: (2025)
Learning New Concepts, Remembering the Old: Continual Learning for Multimodal Concept Bottleneck Models
by: Lai, Songning, et al.
Published: (2024)
by: Lai, Songning, et al.
Published: (2024)
Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts
by: Choi, Jihye, et al.
Published: (2024)
by: Choi, Jihye, et al.
Published: (2024)
Similar Items
-
Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability
by: Oikarinen, Tuomas, et al.
Published: (2025) -
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
by: Javadi, Amirhosein, et al.
Published: (2026) -
Interpreting Neurons in Deep Vision Networks with Language Models
by: Bai, Nicholas, et al.
Published: (2024) -
Linear Explanations for Individual Neurons
by: Oikarinen, Tuomas, et al.
Published: (2024) -
Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
by: Kulkarni, Akshay, et al.
Published: (2025)