”A Comprehensive Survey on Transfer Learning“笔记分为多部分,前文可见: 迁移学习 综述(1):A Comprehensive Survey on Transfer Learning25 赞同 · 0 评论文章 迁移学习 综述(2):A Comprehensive Survey on Transfer Learning15 赞同 · 0 评论文章 4. DATA- BASED INTERPRETATION 基于数据的解释 许多迁移学习...
”A survey on transfer learning“中分为:转导迁移学习(transductive transfer learning),归纳迁移学习(inductive transfer learning)和无监督迁移学习(unsupervised transfer learning),具体定义可见杨强教授”A survey on transfer learning“原文。该分类标准可以从标签设置方面进行解释:转导迁移学习指的是标签信息仅来自源...
This survey\nattempts to connect and systematize the existing transfer learning researches,\nas well as to summarize and interpret the mechanisms and the strategies of\ntransfer learning in a comprehensive way, which may help readers have a better\nunderstanding of the current research status and ...
arXiv在2019年12月4号上传的关于GNN综述论文“A Comprehensive Survey on Graph Neural Networks“。 摘要:近年来,深度学习彻底改变了许多机器学习任务,从图像分类和视频处理到语音识别和自然语言理解。这些任务的数据通常在欧氏空间中表示。但是,越来越多的应用程序从非欧域生成数据,并将数据表示为具有目标之间复杂关系...
A comprehensive survey on transfer learning. Proc IEEE. 2020;109:43–76. Article Google Scholar Wilson G, Cook DJ. A survey of unsupervised deep domain adaptation. ACM Trans Intell Syst Technol (TIST). 2020;11:1–46. Article CAS Google Scholar Goodfellow I, Pouget-Abadie J, Mirza M,...
Khoshgoftaar A survey on heterogeneous transfer learning J Big Data, 4 (1) (2017), 10.1186/s40537-017-0089-0 Google Scholar [45] F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, et al. A comprehensive survey on transfer learning Proc IEEE, 109 (1) (2021), pp. 43-76...
Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. In: Proceedings of the IEEE 109(1), pp. 43–76 Download references Acknowledgements This research was supported by an Ammodo Humanities Award 2017 (Hekster). Author infor...
最新迁移学习综述论文(A Comprehensive Survey on Transfer Learning)- 中科院.zip 迁移学习(Transfer Learning)作为近年来的研究热点之一,受到了广泛关注,每年在各大会议上都有大量的相关文章发表。由于其广泛的应用前景,迁移学习已经成为机器学习中一个热门和有前途的领域。这篇新出论文对近几年迁移学习进行了全面综述...
“Transfer learning from multiple source domains via consensus regularization”一文的工作提出了consensus regularization framework(CRF)。CRF被设计用于多源迁移学习且目标域实例没有被标签。该框架构造了对应于每个源域的mS个分类器,这些分类器需要在目标域上达成共识。每个源域分类器(fkS;k=1,...,mS)的目标函数...
A Comprehensive Survey on Transfer Learning. Proc. IEEE 2021, 109, 43–76. [Google Scholar] [CrossRef] Yang, B.; Lei, Y.; Jia, F.; Xing, S. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mech. Syst. Signal Process. ...