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Titlebook: Continual Semi-Supervised Learning; First International Fabio Cuzzolin,Kevin Cannons,Vincenzo Lomonaco Conference proceedings 2022 The Edi

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发表于 2025-3-21 18:52:04 | 显示全部楼层 |阅读模式
书目名称Continual Semi-Supervised Learning
副标题First International
编辑Fabio Cuzzolin,Kevin Cannons,Vincenzo Lomonaco
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Continual Semi-Supervised Learning; First International  Fabio Cuzzolin,Kevin Cannons,Vincenzo Lomonaco Conference proceedings 2022 The Edi
描述.This book constitutes the proceedings of the First International Workshop on Continual Semi-Supervised Learning, CSSL 2021, which took place as a virtual event during August 2021.The 9 full papers and 0 short papers included in this book were carefully reviewed and selected from 14 submissions..
出版日期Conference proceedings 2022
关键词artificial intelligence; clustering algorithms; computer hardware; computer networks; computer systems; c
版次1
doihttps://doi.org/10.1007/978-3-031-17587-9
isbn_softcover978-3-031-17586-2
isbn_ebook978-3-031-17587-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

书目名称Continual Semi-Supervised Learning影响因子(影响力)




书目名称Continual Semi-Supervised Learning影响因子(影响力)学科排名




书目名称Continual Semi-Supervised Learning网络公开度




书目名称Continual Semi-Supervised Learning网络公开度学科排名




书目名称Continual Semi-Supervised Learning被引频次




书目名称Continual Semi-Supervised Learning被引频次学科排名




书目名称Continual Semi-Supervised Learning年度引用




书目名称Continual Semi-Supervised Learning年度引用学科排名




书目名称Continual Semi-Supervised Learning读者反馈




书目名称Continual Semi-Supervised Learning读者反馈学科排名




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发表于 2025-3-21 21:06:30 | 显示全部楼层
https://doi.org/10.1007/978-3-319-20022-4s reduces the computational burden of the FCC and allows to obtain a better performance with the same amount of data. Simulation results using the collaborative UR-10 robot and a jaw gripper are reported to show the quality of the proposed method.
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https://doi.org/10.1007/978-3-319-20022-4etting problem. KIERA does not exploit any labelled samples for model updates while featuring a task-agnostic merit. The advantage of KIERA has been numerically validated in popular continual learning problems where it shows highly competitive performance compared to state-of-the art approaches. Our implementation is available in ..
发表于 2025-3-22 08:09:26 | 显示全部楼层
,Transfer and Continual Supervised Learning for Robotic Grasping Through Grasping Features,s reduces the computational burden of the FCC and allows to obtain a better performance with the same amount of data. Simulation results using the collaborative UR-10 robot and a jaw gripper are reported to show the quality of the proposed method.
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https://doi.org/10.1007/978-3-319-20194-8r through a distillation process which compresses a large dataset into a tiny set of informative examples. We show the effectiveness of our Distilled Replay against popular replay-based strategies on four Continual Learning benchmarks.
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