mlsanityData to 7 Oct 2026

Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation

arXiv 2109.01369 ↗First posted 3 Sep 2021cs.LGcited by 9

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Abstract (arXiv)

Deep neural networks have demonstrated remarkable performance in many data-driven and prediction-oriented applications, and sometimes even perform better than humans. However, their most significant drawback is the lack of interpretability, which makes them less attractive in many real-world applications. When relating to the moral problem or the environmental factors that are uncertain such as crime judgment, financial analysis, and medical diagnosis, it is essential to mine the evidence for the model's prediction (interpret model knowledge) to convince humans. Thus, investigating how to interpret model knowledge is of paramount importance for both academic research and real applications.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

27 of 49 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
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  7. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  8. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  9. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  10. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
  11. 2019The many Shapley values for model explanationcited by 341
  12. 2019On Completeness-aware Concept-Based Explanations in Deep Neural Networkscited by 239
15 more
  1. 2020Problems with Shapley-value-based explanations as feature importance measurescited by 198
  2. 2016Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layerscited by 197
  3. 2017Visualizing Deep Neural Network Decisions: Prediction Difference Analysiscited by 166
  4. 2019Explaining Classifiers with Causal Concept Effect (CaCE)cited by 135
  5. 2019Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximationcited by 115
  6. 2019Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainabilitycited by 114
  7. 2020Explainable deep learning models in medical image analysiscited by 111
  8. 2018L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Datacited by 100
  9. 2019CXPlain: Causal Explanations for Model Interpretation under Uncertaintycited by 98
  10. 2020Neuron Shapley: Discovering the Responsible Neuronscited by 98
  11. 2020Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sensecited by 62
  12. 2020SCOUT: Self-aware Discriminant Counterfactual Explanationscited by 52
  13. 2021Shapley Counterfactual Credits for Multi-Agent Reinforcement Learningcited by 37
  14. 2016Gradients of Counterfactualscited by 31
  15. 2019Knowledge Consistency between Neural Networks and Beyondcited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 0; 2026: 1.

9 papers.

  1. 2023Multi-dimensional concept discovery (MCD): A unifying framework with completeness guaranteescited by 48
  2. 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanationcited by 37
  3. 2024Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Predictioncited by 10
  4. 2024Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Modelscited by 6
  5. 2022Sparse Subspace Clustering for Concept Discovery (SSCCD)cited by 4
  6. 2024Multi-Depth Concept Extraction for Post-Hoc Vision Encoder Explanationcited by 2
  7. 2022Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neuronscited by 0
  8. 2024Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Modelcited by 0
  9. 2026Post-hoc Self-explanation of CNNscited by 0