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pro vyhledávání: '"Tejero, Javier Gamazo"'
Autor:
Ghamsarian, Negin, Tejero, Javier Gamazo, Neila, Pablo Márquez, Wolf, Sebastian, Zinkernagel, Martin, Schoeffmann, Klaus, Sznitman, Raphael
Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have b
Externí odkaz:
http://arxiv.org/abs/2307.16660
Autor:
Tejero, Javier Gamazo, Zinkernagel, Martin S., Wolf, Sebastian, Sznitman, Raphael, Neila, Pablo Márquez
Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly. For segmentation applications, the burden is particularly high as manual delineations of relevant image content are often extremely expensive or can only be do
Externí odkaz:
http://arxiv.org/abs/2303.11678
Autor:
Tejero, Javier Gamazo1 (AUTHOR) javier.gamazo-tejero@unibe.ch, Neila, Pablo Márquez1 (AUTHOR), Kurmann, Thomas1 (AUTHOR), Gallardo, Mathias1 (AUTHOR), Zinkernagel, Martin2 (AUTHOR), Wolf, Sebastian2 (AUTHOR), Sznitman, Raphael1 (AUTHOR)
Publikováno v:
Scientific Reports. 11/11/2023, Vol. 13 Issue 1, p1-11. 11p.
Akademický článek
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