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of 7
pro vyhledávání: '"Toubhans, Antoine"'
Autor:
Boudiaf, Malik, Bennequin, Etienne, Tami, Myriam, Toubhans, Antoine, Piantanida, Pablo, Hudelot, Céline, Ayed, Ismail Ben
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore th
Externí odkaz:
http://arxiv.org/abs/2301.08390
Autor:
Boudiaf, Malik, Bennequin, Etienne, Tami, Myriam, Hudelot, Celine, Toubhans, Antoine, Piantanida, Pablo, Ayed, Ismail Ben
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously detecting instances that do not belong to any known class. Departing from
Externí odkaz:
http://arxiv.org/abs/2206.09236
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these current benchmarks do not accurately represent the real industrial use ca
Externí odkaz:
http://arxiv.org/abs/2205.05155
Few-Shot Learning (FSL) algorithms have made substantial progress in learning novel concepts with just a handful of labelled data. To classify query instances from novel classes encountered at test-time, they only require a support set composed of a
Externí odkaz:
http://arxiv.org/abs/2105.11804
Publikováno v:
Leveraging Applications of Formal Methods, Verification & Validation. Specialized Techniques & Applications: Part II; 2014, p489-492, 4p
Publikováno v:
Static Analysis: 21st International Symposium, SAS 2014, Munich, Germany, September 11-13, 2014. Proceedings; 2014, p285-301, 17p
Publikováno v:
Verification, Model Checking & Abstract Interpretation (9783642358722); 2013, p375-395, 21p