Corpus ID: 235349029

Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors

@inproceedings{Zhang2021InvertibleCE,
  title={Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors},
  author={Ruihan Zhang and Prashan Madumal and Tim Miller and Krista A. Ehinger and Benjamin I. P. Rubinstein},
  booktitle={AAAI},
  year={2021}
}
Convolutional neural network (CNN) models for computer vision are powerful but lack explainability in their most basic form. This deficiency remains a key challenge when applying CNNs in important domains. Recent work on explanations through feature importance of approximate linear models has moved from input-level features (pixels or segments) to features from mid-layer feature maps in the form of concept activation vectors (CAVs). CAVs contain concept-level information and could be learned… Expand

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