Learning Bottleneck Concepts in Image Classification
Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel relevance to the decision. Yet, interpreting such explanations may require expert knowledge. Some recent attempts toward interpretability adopt a concept-based framework, giving a higher-level relationship between some concepts and model decisions. This paper proposes Bottleneck Concept Learner (BotCL), which represents an image solely by the presence/absence of concepts learned through training over the target task without explicit supervision over the concepts. It uses self-supervision and tailored regularizers so that learned concepts can be human-understandable. Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability. Code is available at https://github.com/wbw520/BotCL and a simple demo is available at https://botcl.liangzhili.com/.
Code (1)
Tasks
Classificationimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer
Concept Bottleneck Models (CBMs) offer inherent interpretability by initially translating images into human-comprehensible concepts, followed by a linear combination of these concepts for classification. However, the ann…
Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models
Concept Bottleneck Models (CBMs) decompose image classification into a process governed by interpretable, human-readable concepts. Recent advances in CBMs have used Large Language Models (LLMs) to generate candidate conc…
image-classificationImage ClassificationLanguage in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification
Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-sta…
image-classificationImage ClassificationLanguage ModelingLanguage ModellingConcepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification
Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but extending this paradigm to video remains challenging due to the difficulty o…
Video ClassificationImage ClassificationMitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
Ensuring fairness in image classification prevents models from perpetuating and amplifying bias. Concept bottleneck models (CBMs) map images to high-level, human-interpretable concepts before making predictions via a spa…
Image Classification