# Learning Bottleneck Concepts in Image Classification

arXiv: https://arxiv.org/abs/2304.10131 · HTML: https://mlsanity.com/paper/2304.10131/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 51 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Understanding Deep Networks via Extremal Perturbations and Smooth Masks](https://mlsanity.com/paper/1910.08485/) (2019)
- [On Completeness-aware Concept-Based Explanations in Deep Neural Networks](https://mlsanity.com/paper/1910.07969/) (2019)
- [Object Detectors Emerge in Deep Scene CNNs](https://mlsanity.com/paper/1412.6856/) (2014)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [Restricting the Flow: Information Bottlenecks for Attribution](https://mlsanity.com/paper/2001.00396/) (2020)
- [The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](https://mlsanity.com/paper/1907.09294/) (2019)
- [Interpretable and Accurate Fine-grained Recognition via Region Grouping](https://mlsanity.com/paper/2005.10411/) (2020)
- [Learning Semantic Concepts and Order for Image and Sentence Matching](https://mlsanity.com/paper/1712.02036/) (2017)
- [Interpretability Beyond Classification Output: Semantic Bottleneck Networks](https://mlsanity.com/paper/1907.10882/) (2019)

## Built on by (most cited first, 30 of 49)

- [Incremental Residual Concept Bottleneck Models](https://mlsanity.com/paper/2404.08978/) (2024)
- [Language Guided Concept Bottleneck Models for Interpretable Continual Learning](https://mlsanity.com/paper/2503.23283/) (2025)
- [RWKV-CLIP: A Robust Vision-Language Representation Learner](https://mlsanity.com/paper/2406.06973/) (2024)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [DCBM: Data-Efficient Visual Concept Bottleneck Models](https://mlsanity.com/paper/2412.11576/) (2024)
- [Neural Concept Binder](https://mlsanity.com/paper/2406.09949/) (2024)
- [Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models](https://mlsanity.com/paper/2502.20134/) (2025)
- [Interpretable Image Classification via Non-parametric Part Prototype Learning](https://mlsanity.com/paper/2503.10247/) (2025)
- [MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes](https://mlsanity.com/paper/2404.08968/) (2024)
- [Evaluating Readability and Faithfulness of Concept-based Explanations](https://mlsanity.com/paper/2404.18533/) (2024)
- [Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts](https://mlsanity.com/paper/2412.14097/) (2024)
- [Graph Concept Bottleneck Models](https://mlsanity.com/paper/2508.14255/) (2025)
- [Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery](https://mlsanity.com/paper/2404.10356/) (2024)
- [Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Images](https://mlsanity.com/paper/2407.19719/) (2024)
- [Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace](https://mlsanity.com/paper/2305.15775/) (2023)
- [MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning](https://mlsanity.com/paper/2312.14574/) (2023)
- [Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models](https://mlsanity.com/paper/2406.12649/) (2024)
- [Visual Data Diagnosis and Debiasing with Concept Graphs](https://mlsanity.com/paper/2409.18055/) (2024)
- [Learning Interpretable Queries for Explainable Image Classification with Information Pursuit](https://mlsanity.com/paper/2312.11548/) (2023)
- [Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization](https://mlsanity.com/paper/2306.04893/) (2023)
- [Understanding Multimodal Deep Neural Networks: A Concept Selection View](https://mlsanity.com/paper/2404.08964/) (2024)
- [Improving Concept Alignment in Vision-Language Concept Bottleneck Models](https://mlsanity.com/paper/2405.01825/) (2024)
- [A Comprehensive Survey on the Risks and Limitations of Concept-based Models](https://mlsanity.com/paper/2506.04237/) (2025)
- [Interpretable Decision-Making for End-to-End Autonomous Driving](https://mlsanity.com/paper/2508.18898/) (2025)
- [Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning](https://mlsanity.com/paper/2402.14726/) (2024)
- [A Self-explaining Neural Architecture for Generalizable Concept Learning](https://mlsanity.com/paper/2405.00349/) (2024)
- [SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation](https://mlsanity.com/paper/2310.07698/) (2023)
- [Explainable Image Recognition via Enhanced Slot-attention Based Classifier](https://mlsanity.com/paper/2407.05616/) (2024)
- [TAB: Transformer Attention Bottlenecks enable User Intervention and Debugging in Vision-Language Models](https://mlsanity.com/paper/2412.18675/) (2024)
- [A Survey on Interpretability in Visual Recognition](https://mlsanity.com/paper/2507.11099/) (2025)

## Abstract (arXiv)

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/.