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ORCIDCamille Garcin
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2020 – today
- 2023
[b1]Camille Garcin:
Loss functions for set-valued classification. (Fonctions de perte pour la classification à valeurs d'ensembles). University of Montpellier, France, 2023- [i2]Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon:
A two-head loss function for deep Average-K classification. CoRR abs/2303.18118 (2023) - 2022
- [c3]Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon:
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification. ICML 2022: 7208-7222 - [i1]Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon:
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification. CoRR abs/2202.02193 (2022) - 2021
- [c2]Camille Garcin, Alexis Joly, Pierre Bonnet, Antoine Affouard, Jean-Christophe Lombardo, Mathias Chouet, Maximilien Servajean, Titouan Lorieul, Joseph Salmon:
Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution. NeurIPS Datasets and Benchmarks 2021
2010 – 2019
- 2019
- [c1]Khalil Ouardini, Huijuan Yang
, Balagopal Unnikrishnan, Manon Romain, Camille Garcin, Houssam Zenati, J. Peter Campbell, Michael F. Chiang, Jayashree Kalpathy-Cramer, Vijay Chandrasekhar, Pavitra Krishnaswamy, Chuan-Sheng Foo:
Towards Practical Unsupervised Anomaly Detection on Retinal Images. DART/MIL3ID@MICCAI 2019: 225-234
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