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pro vyhledávání: '"'Bengar, Javad Zolfaghari"'
Active learning aims to reduce the labeling effort that is required to train algorithms by learning an acquisition function selecting the most relevant data for which a label should be requested from a large unlabeled data pool. Active learning is ge
Externí odkaz:
http://arxiv.org/abs/2110.04543
Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amo
Externí odkaz:
http://arxiv.org/abs/2108.11458
Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the
Externí odkaz:
http://arxiv.org/abs/2107.14707
Autor:
Bengar, Javad Zolfaghari, Gonzalez-Garcia, Abel, Villalonga, Gabriel, Raducanu, Bogdan, Aghdam, Hamed H., Mozerov, Mikhail, Lopez, Antonio M., van de Weijer, Joost
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort
Externí odkaz:
http://arxiv.org/abs/1908.11757
Autor:
'Bengar, Javad Zolfaghari
Publikováno v:
Gonzalez-Garcia, Abel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=r3c4b2081b22::e731c6101ca1ec313e6b64da22ff71ed
