Historical benchmark Dataset for multiple computer vision tasks: Discuss limitations of different Machine Learning techniques

Authors

Fabienne Vial-Bonacci
Amaury Habrard (ORCID: https://orcid.org/0000-0003-3038-9347)
T. Gautrais
Rémi Emonet (ORCID: https://orcid.org/0000-0002-1870-1329)
Thierry Fournel (ORCID: https://orcid.org/0000-0002-1613-4594)
Vincent Ventresque
Sayan Kumar Chaki (ORCID: https://orcid.org/0000-0003-1390-1329)
Christelle Bahier-Porte

Institutions

École Normale Supérieure de Lyon (FR)
Institut Universitaire de France (FR)
Laboratoire Hubert Curien (FR)
Institut d'Histoire des Représentations et des Idées dans les Modernités (FR)
Triangle : Action, Discours, Pensée politique et économique (FR)
Université Jean Monnet (FR)

Publication Details

Journal: SPIRE - Sciences Po Institutional REpository
Published: 2024-11-14
Primary Topic: Explainable Artificial Intelligence (XAI)
Type: article
Field-Weighted Citation Impact: 0.00

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article

Historical benchmark Dataset for multiple computer vision tasks: Discuss limitations of different Machine Learning techniques

Fabienne Vial-Bonacci, Amaury Habrard, T. Gautrais, Rémi Emonet et al.

Nov 14, 2024

SPIRE - Sciences Po Institutional REpository

Explainable Artificial Intelligence (XAI)

article

Historical benchmark Dataset for multiple computer vision tasks: Discuss limitations of different Machine Learning techniques

Fabienne Vial-Bonacci, Amaury Habrard, T. Gautrais, Rémi Emonet, Thierry Fournel, Vincent Ventresque, Sayan Kumar Chaki, Christelle Bahier-Porte

November 14, 2024article en

Abstract

No abstract available for this paper.

SPIRE - Sciences Po Institutional REpository

École Normale Supérieure de Lyon (FR), Institut Universitaire de France (FR), Laboratoire Hubert Curien (FR), Institut d'Histoire des Représentations et des Idées dans les Modernités (FR), Triangle : Action, Discours, Pensée politique et économique (FR), Université Jean Monnet (FR)

Openalex Percentile: Top 40%

Explainable Artificial Intelligence (XAI)

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