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Overview of computer vision supervised learning techniques for low-data training

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dc.contributor.author BURLACU, Alexandru
dc.date.accessioned 2019-11-02T11:25:00Z
dc.date.available 2019-11-02T11:25:00Z
dc.date.issued 2019
dc.identifier.citation BURLACU, Alexandru. Overview of computer vision supervised learning techniques for low-data training. In: Electronics, Communications and Computing: extended abstracts of the 10th Intern. Conf.: the 55th anniversary of Technical University of Moldova, Chişinău, October 23-26, 2019. Chişinău, 2019, p. 44. ISBN 978-9975-108-84-3. en_US
dc.identifier.isbn 978-9975-108-84-3
dc.identifier.uri http://repository.utm.md/handle/5014/5902
dc.description Abstract en_US
dc.description.abstract This work is an overview of techniques of varying complexity and novelty for supervised, or rather weakly supervised learning for computer vision algorithms. With the advent of deep learning the number of organizations and practitioners who think that they can solve problems using it also grows. Deep learning algorithms normally require vast amounts of labeled data, but depending on the domain it is not always possible to have a well annotated huge dataset, just think about healthcare. en_US
dc.language.iso en en_US
dc.publisher Tehnica UTM en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject knowledge distillation en_US
dc.subject knowledge transfer en_US
dc.subject self-supervised learning en_US
dc.title Overview of computer vision supervised learning techniques for low-data training en_US
dc.type Article en_US


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  • 2019
    Extended Abstracts of the: The 10th IC|ECCO; October 23-26, 2019

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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