7 lines
666 B
Markdown
Executable File
7 lines
666 B
Markdown
Executable File
# Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
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- **Authors**: Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
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- **Year**: 2018
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- **Summary**: TCAV moves beyond explaining predictions in terms of low-level features and instead explains them in terms of high-level, human-understandable concepts. It quantifies the degree to which a user-defined concept (e.g., 'stripes' for a zebra classifier) is important to a model's prediction for a class of inputs. This allows for more global and intuitive explanations.
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- **Link**: https://arxiv.org/abs/1711.11279
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