Cover Image

Explanation Bottleneck Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yamaguchi, Shin'ya, Nishida, Kosuke
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Zero-shot Concept Bottleneck Models
by: Yamaguchi, Shin'ya, et al.
Published: (2025)

Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought
by: Yamaguchi, Shin'ya, et al.
Published: (2025)

Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations
by: Adachi, Kazuki, et al.
Published: (2022)

Parallel In-context Learning for Large Vision Language Models
by: Yamaguchi, Shin'ya, et al.
Published: (2026)

Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
by: Yamaguchi, Shin'ya, et al.
Published: (2024)

Post-pre-training for Modality Alignment in Vision-Language Foundation Models
by: Yamaguchi, Shin'ya, et al.
Published: (2025)

Machine Learning Modeling for Multi-order Human Visual Motion Processing
by: Sun, Zitang, et al.
Published: (2025)

Understanding Pure Textual Reasoning for Blind Image Quality Assessment
by: Li, Yuan, et al.
Published: (2026)

HAPI: A Model for Learning Robot Facial Expressions from Human Preferences
by: Yang, Dongsheng, et al.
Published: (2025)

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
by: Wang, Ying, et al.
Published: (2023)

Editable Concept Bottleneck Models
by: Hu, Lijie, et al.
Published: (2024)

Semi-supervised Concept Bottleneck Models
by: Hu, Lijie, et al.
Published: (2024)

Process-Guided Concept Bottleneck Model
by: Asiyabi, Reza M., et al.
Published: (2026)

Guiding Perception-Reasoning Closer to Human in Blind Image Quality Assessment
by: Li, Yuan, et al.
Published: (2025)

Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts
by: Choi, Jihye, et al.
Published: (2024)

MultiModal Fine-tuning with Synthetic Captions
by: Enomoto, Shohei, et al.
Published: (2026)

Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
by: Singhi, Nishad, et al.
Published: (2024)

Multimodal Foundation Model for Cross-Modal Retrieval and Activity Recognition Tasks
by: Matsuishi, Koki, et al.
Published: (2025)

Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?
by: Furby, Jack, et al.
Published: (2024)

Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
by: Xu, Xinyue, et al.
Published: (2024)

TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks
by: Hu, Yuanze, et al.
Published: (2025)

Towards a Mechanistic Explanation of Diffusion Model Generalization
by: Niedoba, Matthew, et al.
Published: (2024)

Studying How to Efficiently and Effectively Guide Models with Explanations
by: Rao, Sukrut, et al.
Published: (2023)

Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching
by: Enomoto, Shohei, et al.
Published: (2024)

LINE: LLM-based Iterative Neuron Explanations for Vision Models
by: Zaigrajew, Vladimir, et al.
Published: (2026)

EVO-LRP: Evolutionary Optimization of LRP for Interpretable Model Explanations
by: Zhang, Emerald, et al.
Published: (2025)

The Perceptual Bandwidth Bottleneck in Vision-Language Models: Active Visual Reasoning via Sequential Experimental Design
by: Liu, Anjie, et al.
Published: (2026)

Leveraging Local Structure for Improving Model Explanations: An Information Propagation Approach
by: Yang, Ruo, et al.
Published: (2024)

Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
by: Wang, Hengyi, et al.
Published: (2024)

Extreme Blind Image Restoration via Prompt-Conditioned Information Bottleneck
by: Kim, Hongeun, et al.
Published: (2025)

When Better Eyes Lead to Blindness: A Diagnostic Study of the Information Bottleneck in CNN-LSTM Image Captioning Models
by: Gupta, Hitesh Kumar
Published: (2025)

Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs
by: Xu, Kaiwen, et al.
Published: (2023)

Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding
by: Li, Depeng, et al.
Published: (2024)

Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery
by: Rao, Sukrut, et al.
Published: (2024)

Fwd2Bot: LVLM Visual Token Compression with Double Forward Bottleneck
by: Bulat, Adrian, et al.
Published: (2025)

MEGL: Multimodal Explanation-Guided Learning
by: Zhang, Yifei, et al.
Published: (2024)

Guaranteed Optimal Compositional Explanations for Neurons
by: La Rosa, Biagio, et al.
Published: (2025)

Difference Vector Equalization for Robust Fine-tuning of Vision-Language Models
by: Suzuki, Satoshi, et al.
Published: (2025)

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption
by: Adachi, Kazuki, et al.
Published: (2025)

On Spectral Properties of Gradient-based Explanation Methods
by: Mehrpanah, Amir, et al.
Published: (2025)