Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

30 November 2017

Justin Gilmer

Papers citing "Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)"

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Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 21 18 0 10 Dec 2020
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Debiasing Convolutional Neural Networks via Meta Orthogonalization Kurtis Evan David Qiang Liu Ruth C. Fong FaML 15 3 0 15 Nov 2020
One Explanation is Not Enough: Structured Attention Graphs for Image Classification Vivswan Shitole Li Fuxin Minsuk Kahng Prasad Tadepalli Alan Fern FAtt GNN 6 38 0 13 Nov 2020
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Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 36 7 0 23 Oct 2020
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A Framework to Learn with Interpretation Jayneel Parekh Pavlo Mozharovskyi Florence dÁlché-Buc AI4CE FAtt 17 30 0 19 Oct 2020
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The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt 15 23 0 23 Sep 2020
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Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset Erico Tjoa Cuntai Guan XAI FAtt 11 27 0 07 Sep 2020
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