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Publikováno v:
IEEE Access, Vol 11, Pp 116706-116720 (2023)
Self-supervised learning (SSL) has emerged as a promising approach for learning representations from unlabeled data. Momentum-based contrastive frameworks such as MoCo-v3 have shown remarkable success among the many SSL methods proposed in recent yea
Externí odkaz:
https://doaj.org/article/40a6e2d1b58f4821815151218f40b9d6
Publikováno v:
IEEE Transactions on Circuits and Systems for Video Technology. 32:5137-5150
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200557
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46a84fec24be2779ba2d353358cf84dc
https://doi.org/10.1007/978-3-031-20056-4_42
https://doi.org/10.1007/978-3-031-20056-4_42
Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch siz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6c0f553468d35a3e4e108cb7daaf0b6
Publikováno v:
Pattern Recognition. 120:108082
Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurrin