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Title: GitHub - monvdust/transferlearning: Resources and codes about transfer learning and domain adaptation--迁移学习 · GitHub

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Description: Resources and codes about transfer learning and domain adaptation--迁移学习 - monvdust/transferlearning

Open Graph Description: Resources and codes about transfer learning and domain adaptation--迁移学习 - monvdust/transferlearning

X Description: Resources and codes about transfer learning and domain adaptation--迁移学习 - monvdust/transferlearning

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https://github.com/monvdust/transferlearning#关于迁移学习的一些资料
行为识别https://github.com/jindongwang/activityrecognition
机器学习https://github.com/jindongwang/MachineLearning
https://github.com/monvdust/transferlearning#目录-table-of-contents
最新 Latesthttps://github.com/monvdust/transferlearning#0latest
迁移学习简介 Introduction to transfer learninghttps://github.com/monvdust/transferlearning#1%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%AE%80%E4%BB%8B
迁移学习的综述文章 Survey papershttps://github.com/monvdust/transferlearning#2%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E7%BB%BC%E8%BF%B0%E6%96%87%E7%AB%A0
迁移学习相关代码 Available codeshttps://github.com/jindongwang/transferlearning/tree/master/code
迁移学习代表性研究学者 Scholarshttps://github.com/monvdust/transferlearning#4%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E4%BB%A3%E8%A1%A8%E6%80%A7%E7%A0%94%E7%A9%B6%E5%AD%A6%E8%80%85
迁移学习相关的硕博士论文 Thesishttps://github.com/monvdust/transferlearning#5%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E7%9A%84%E7%A1%95%E5%8D%9A%E5%A3%AB%E8%AE%BA%E6%96%87
代表性文章阅读 Paper readinghttps://github.com/monvdust/transferlearning#6%E4%BB%A3%E8%A1%A8%E6%80%A7%E6%96%87%E7%AB%A0%E9%98%85%E8%AF%BB
迁移学习用于行为识别 Transfer learning for activity recognitionhttps://github.com/jindongwang/activityrecognition/blob/master/notes/%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%94%A8%E4%BA%8E%E8%A1%8C%E4%B8%BA%E8%AF%86%E5%88%AB.md
常用数据集 Datasetshttps://github.com/jindongwang/transferlearning/blob/master/doc/dataset.md
Contributinghttps://github.com/monvdust/transferlearning#contributing
https://github.com/monvdust/transferlearning#0latest
基于深度学习和迁移学习的识花实践https://cosx.org/2017/10/transfer-learning/
Learning Transferable Architectures for Scalable Image Recognitionhttps://arxiv.org/abs/1707.07012
Domain Adaptation in Computer Vision Applicationshttps://books.google.com.hk/books?id=7181DwAAQBAJ&pg=PA95&lpg=PA95&dq=Learning+Domain+Invariant+Embeddings+by+Matching%E2%80%A6&source=bl&ots=fSc1yvZxU3&sig=XxmGZkrfbJ2zSsJcsHhdfRpjaqk&hl=zh-CN&sa=X&ved=0ahUKEwjzvODqkI3XAhUCE5QKHYStBywQ6AEIRDAE#v=onepage&q=Learning%20Domain%20Invariant%20Embeddings%20by%20Matching%E2%80%A6&f=false
Adversarial Representation Learning For Domain Adaptationhttps://arxiv.org/abs/1707.01217
Mutual Alignment Transfer Learninghttps://arxiv.org/abs/1707.07907
Learning Invariant Riemannian Geometric Representations Using Deep Netshttps://arxiv.org/abs/1708.09485
Learning To Transferhttps://arxiv.org/abs/1708.05629
我的解读https://zhuanlan.zhihu.com/p/28888554
Deep Transfer Learning with Joint Adaptation Networkshttps://2017.icml.cc/Conferences/2017/Schedule?showEvent=1117
代码https://github.com/thuml/transfer-caffe
https://github.com/monvdust/transferlearning#1迁移学习简介
文档https://github.com/jindongwang/transferlearning/blob/master/doc/%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%AE%80%E4%BB%8B.md
PPT(英文)http://jd92.wang/assets/files/l03_transferlearning.pdf
PPT(中文)http://jd92.wang/assets/files/l08_tl_zh.pdf
https://www.youtube.com/watch?v=qD6iD4TFsdQhttps://www.youtube.com/watch?v=qD6iD4TFsdQ
迁移学习领域的著名学者、代表工作及实验室介绍https://github.com/jindongwang/transferlearning/blob/master/doc/scholar_TL.md
负迁移(negative transfer)https://www.zhihu.com/question/66492194/answer/242870418
https://github.com/monvdust/transferlearning#2迁移学习的综述文章
一些迁移学习的综述文章https://pan.baidu.com/s/1c19TTfU
A survey on transfer learninghttps://pan.baidu.com/s/1i5AFO7z
Cross-dataset recognition: a surveyhttps://arxiv.org/abs/1705.04396
A survey on multi-task learninghttps://arxiv.org/abs/1707.08114
A survey of transfer learninghttps://pan.baidu.com/s/1gfgXLXT
迁移学习应用于行为识别https://pan.baidu.com/s/1kVABOYr
迁移学习与增强学习https://pan.baidu.com/s/1slfr0w1
多个源域进行迁移的综述https://pan.baidu.com/s/1eSGREF4
视觉domain adaptation综述https://pan.baidu.com/s/1o8BR7Vc
迁移学习研究进展https://pan.baidu.com/s/1bpautob
理论分析https://pan.baidu.com/s/1jIotlTc
https://github.com/monvdust/transferlearning#3代码
这里https://github.com/jindongwang/transferlearning/tree/master/code
https://github.com/monvdust/transferlearning#4迁移学习代表性研究学者
这里https://github.com/jindongwang/transferlearning/blob/master/doc/scholar_TL.md
Qiang Yanghttp://www.cs.ust.hk/~qyang/
Google scholarhttps://scholar.google.com/citations?user=1LxWZLQAAAAJ&hl=zh-CN
Sinno Jialin Panhttp://www.ntu.edu.sg/home/sinnopan/
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Fuzhen Zhuanghttp://www.intsci.ac.cn/users/zhuangfuzhen/
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https://github.com/monvdust/transferlearning#5迁移学习相关的硕博士论文
Feature-based Transfer Learning and Its Applicationshttps://pan.baidu.com/s/1bUqMfW
Kernel Methods for Unsupervised Domain Adaptationhttps://pan.baidu.com/s/1bpbawv9
Learning based Activity Recognitionhttps://pan.baidu.com/s/1bp2K9HX
Learning with Limited Data in Sensor-based Human Behavior Predictionhttps://pan.baidu.com/s/1o8MbbBk
迁移学习问题与方法研究https://pan.baidu.com/s/1pLiJzIV
基于实例和特征的迁移学习算法研究https://pan.baidu.com/s/1i4Vyygd
自适应行为识别中的迁移学习方法研究https://pan.baidu.com/s/1kVqYXnh
Correlation Alignment for Domain Adaptationhttp://www.cs.uml.edu/~bsun/papers/baochen_phd_thesis.pdf
完整版https://pan.baidu.com/s/1mizKzna
https://github.com/monvdust/transferlearning#6代表性文章阅读
Domain adaptationhttps://github.com/jindongwang/transferlearning/blob/master/doc/domain_adaptation.md
paperweeklyhttp://www.paperweekly.site/collections/231/papers
https://github.com/monvdust/transferlearning#代表性的方法及文章
Domain adaptation via tranfer component analysishttps://mega.nz/#!JTwElLrL!j5-TanhHCMESsGBNvY6I_hX6uspsrTxyopw8bPQ2azU
我的解读https://zhuanlan.zhihu.com/p/26764147?group_id=844611188275965952
Transfer Feature Learning with Joint Distribution Adaptationhttp://ise.thss.tsinghua.edu.cn/~mlong/doc/joint-distribution-adaptation-iccv13.pdf
我的解读https://zhuanlan.zhihu.com/p/27336930
Geodesic flow kernel for unsupervised domain adaptationhttps://mega.nz/#!tDY1lCSD!flMSgl-0uIswpSFL3sdZgKi6fOyFVLtcO8P6SE0OUPU
我的解读https://zhuanlan.zhihu.com/p/27782708
Domain invariant transfer kernel learninghttps://mega.nz/#!tOoCCRhB!YyoorOUcp6XIPPd6A0s7qglYnaSiRJFEQBphtZ2c58Q
我的解读https://zhuanlan.zhihu.com/p/27657910
深度联合适配网络http://proceedings.mlr.press/v70/long17a.html
学习迁移https://arxiv.org/abs/1708.05629
我的解读https://zhuanlan.zhihu.com/p/28888554
Simultaneous Deep Transfer Across Domains and Taskshttps://www.cv-foundation.org/openaccess/content_iccv_2015/html/Tzeng_Simultaneous_Deep_Transfer_ICCV_2015_paper.html
我的解读https://zhuanlan.zhihu.com/p/30621691
迁移学习用于行为识别的文章总结https://github.com/jindongwang/activityrecognition/blob/master/notes/%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%94%A8%E4%BA%8E%E8%A1%8C%E4%B8%BA%E8%AF%86%E5%88%AB.md
https://github.com/monvdust/transferlearning#迁移学习用于行为识别的文章总结
记与迁移学习大牛杨强教授的第二次会面https://zhuanlan.zhihu.com/p/26260083
https://github.com/monvdust/transferlearning#记与迁移学习大牛杨强教授的第二次会面
https://github.com/monvdust/transferlearning#contributing
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