Explainable and interpretable models in computer vision and machine learning - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Ouvrage (Y Compris Édition Critique Et Traduction) Année : 2018

Explainable and interpretable models in computer vision and machine learning

Xavier Baró
  • Fonction : Directeur scientifique
  • PersonId : 990465
Yağmur Güçlütürk
  • Fonction : Directeur scientifique
Umut Güçlü
  • Fonction : Directeur scientifique
  • PersonId : 1025261
Marcel A. J. van Gerven
  • Fonction : Directeur scientifique

Résumé

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.

Dates et versions

hal-01991623 , version 1 (23-01-2019)

Identifiants

Citer

Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yağmur Güçlütürk, et al. (Dir.). 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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