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Explainable and interpretable models in computer vision and machine learning

Abstract : This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision.
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https://hal.inria.fr/hal-01991623
Contributor : Marc Schoenauer <>
Submitted on : Wednesday, January 23, 2019 - 11:09:23 PM
Last modification on : Thursday, July 8, 2021 - 3:50:05 AM

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Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yağmur Güçlütürk, et al.. Explainable and interpretable models in computer vision and machine learning. Springer Verlag, 2018, The Springer Series on Challenges in Machine Learning, 9783319981307. ⟨10.1007/978-3-319-98131-4⟩. ⟨hal-01991623⟩

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