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Title: GitHub - BasicPower/transferlearning: Everything about Transfer Learning and Domain Adaptation--迁移学习 · GitHub

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READMEhttps://github.com/BasicPower/transferlearning/tree/master
https://github.com/BasicPower/transferlearning/tree/master#迁移学习-transfer-learning
https://github.com/BasicPower/transferlearning/tree/master#目录-table-of-contents
最新文章 Latesthttps://github.com/BasicPower/transferlearning/tree/master#0latest
迁移学习简介 Introduction to transfer learninghttps://github.com/BasicPower/transferlearning/tree/master#1%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%AE%80%E4%BB%8B
研究领域与相关文章 Research articles by areahttps://github.com/BasicPower/transferlearning/tree/master#2%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%A0%94%E7%A9%B6%E9%A2%86%E5%9F%9F%E4%B8%8E%E7%9B%B8%E5%85%B3%E6%96%87%E7%AB%A0
理论与综述文章 Theoretical and survey papershttps://github.com/BasicPower/transferlearning/tree/master#3%E7%90%86%E8%AE%BA%E4%B8%8E%E7%BB%BC%E8%BF%B0%E6%96%87%E7%AB%A0
相关代码 Available codeshttps://github.com/BasicPower/transferlearning/tree/master#4%E4%BB%A3%E7%A0%81
迁移学习代表性研究学者 Scholarshttps://github.com/BasicPower/transferlearning/tree/master#5%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
相关的硕博士论文 Thesishttps://github.com/BasicPower/transferlearning/tree/master#6%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
常用公开数据集及算法结果 Datasets and benchmarkhttps://github.com/BasicPower/transferlearning/tree/master#7%E5%85%AC%E5%BC%80%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%AE%9E%E9%AA%8C%E7%BB%93%E6%9E%9C
比赛 Challenges and competitionshttps://github.com/BasicPower/transferlearning/tree/master#8%E6%AF%94%E8%B5%9B
其他 Miscellaneoushttps://github.com/BasicPower/transferlearning/tree/master#%E5%85%B6%E4%BB%96
Contributinghttps://github.com/BasicPower/transferlearning/tree/master#contributing
行为识别https://github.com/jindongwang/activityrecognition
机器学习https://github.com/jindongwang/MachineLearning
https://github.com/BasicPower/transferlearning/tree/master#0latest
迁移学习文章汇总 Awesome transfer learning papershttps://github.com/jindongwang/transferlearning/tree/master/doc/awesome_paper.md
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detectionhttps://arxiv.org/abs/1807.11677
DOCK: Detecting Objects by transferring Common-sense Knowledgehttps://arxiv.org/abs/1804.01077
A Zero-Shot Framework for Sketch-based Image Retrievalhttps://arxiv.org/abs/1807.11724
Dist-GAN: An Improved GAN using Distance Constraintshttps://arxiv.org/abs/1803.08887
Tensorflowhttps://github.com/tntrung/gan
Visual Domain Adaptation with Manifold Embedded Distribution Alignmenthttps://arxiv.org/abs/1807.07258
MEDAhttps://github.com/jindongwang/transferlearning/tree/master/code/traditional/MEDA
Multimodal Deep Domain Adaptationhttps://arxiv.org/abs/1807.11697
Rank and Rate: Multi-task Learning for Recommender Systemshttps://arxiv.org/abs/1807.11698
Meta-learning autoencoders for few-shot predictionhttps://arxiv.org/abs/1807.09912
https://arxiv.org/abs/1706.05335v2https://arxiv.org/abs/1706.05335v2
代码https://github.com/twanvl/rwa-da/tree/master/src
更多 More...https://github.com/jindongwang/transferlearning/tree/master/doc/awesome_paper.md
https://github.com/BasicPower/transferlearning/tree/master#1迁移学习简介
简单的中文简介 Chinese introductionhttps://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(English)http://jd92.wang/assets/files/l03_transferlearning.pdf
PPT(中文)http://jd92.wang/assets/files/l08_tl_zh.pdf
PDFhttp://jd92.wang/assets/files/l12_da.pdf
Videohttp://mp.weixin.qq.com/s?__biz=MzI5MDUyMDIxNA==&mid=2247484940&idx=2&sn=35e64e07fde9a96afbb65dbf40a945eb&chksm=ec1febf5db6862e38d5e02ff3278c61b376932a46c5628c7d9cb1769c572bfd31819c13dd468&mpshare=1&scene=1&srcid=1219JpTNZFiNDCHsTUrUxwqy#rd
PPT(Samsung)http://ise.thss.tsinghua.edu.cn/~mlong/doc/transfer-learning-talk.pdf
PPT(Google China)http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-transfer-learning-talk.pdf
《迁移学习简明手册》Transfer Learning Tutorialhttps://zhuanlan.zhihu.com/p/35352154
开发维护地址https://github.com/jindongwang/transferlearning-tutorial
台湾大学李宏毅的视频讲解(中文视频)https://www.youtube.com/watch?v=qD6iD4TFsdQ
迁移学习中的领域自适应方法(中文)http://mp.weixin.qq.com/s?__biz=MzI5MDUyMDIxNA==&mid=2247484940&idx=2&sn=35e64e07fde9a96afbb65dbf40a945eb&chksm=ec1febf5db6862e38d5e02ff3278c61b376932a46c5628c7d9cb1769c572bfd31819c13dd468&mpshare=1&scene=1&srcid=1219JpTNZFiNDCHsTUrUxwqy#rd
迁移学习领域的著名学者、代表工作及实验室介绍 Transfer Learning Scholars and Labshttps://github.com/jindongwang/transferlearning/blob/master/doc/scholar_TL.md
负迁移(negative transfer)https://www.zhihu.com/question/66492194/answer/242870418
基于深度学习和迁移学习的识花实践 Using Transfer Learning for Flower Recognitionhttps://cosx.org/2017/10/transfer-learning/
基于Pytorch的图像分类 Using Transfer Learning for Image Classificationhttps://github.com/miguelgfierro/sciblog_support/blob/master/A_Gentle_Introduction_to_Transfer_Learning/Intro_Transfer_Learning.ipynb
使用Pytorch进行finetune Using Pytorch for Fine-tunehttps://github.com/Spandan-Madan/Pytorch_fine_tuning_Tutorial
基于AlexNet和ResNet的finetune Fine-tune based on Alexnet and Resnethttps://github.com/jindongwang/transferlearning/tree/master/code/AlexNet_ResNet
https://github.com/BasicPower/transferlearning/tree/master#2迁移学习研究领域与相关文章
领域自适应(非深度) Domain Adaptation (Shallow)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E9%A2%86%E5%9F%9F%E8%87%AA%E9%80%82%E5%BA%94
Domain adaptationhttps://github.com/jindongwang/transferlearning/blob/master/doc/domain_adaptation.md
在线迁移学习 Online transfer learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E5%9C%A8%E7%BA%BF%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
终身迁移学习 Lifelong transfer learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E7%BB%88%E8%BA%AB%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
异构迁移学习 Heterogeneous Transfer Learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E5%BC%82%E6%9E%84%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
深度迁移学习 Deep Transfer Learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E6%B7%B1%E5%BA%A6%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
深度对抗迁移迁移学习 Deep Adversarial transfer learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E5%AF%B9%E6%8A%97%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
传递迁移学习 Transitive Transfer Learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E4%BC%A0%E9%80%92%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
强化迁移学习 Transfer Learning with Reinforcement Learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E5%BC%BA%E5%8C%96%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
应用 Applicationshttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#%E5%BA%94%E7%94%A8
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
paperweeklyhttp://www.paperweekly.site/collections/231/papers
https://github.com/BasicPower/transferlearning/tree/master#3理论与综述文章
A survey on transfer learninghttp://ieeexplore.ieee.org/abstract/document/5288526/
Analysis of Representations for Domain Adaptationhttps://dl.acm.org/citation.cfm?id=2976474
A Theory of Learning from Different Domainshttps://link.springer.com/article/10.1007/s10994-009-5152-4
Learning Bounds for Domain Adaptationhttp://papers.nips.cc/paper/3212-learning-bounds-for-domain-adaptation
A Hilbert Space Embedding for Distributionshttps://link.springer.com/chapter/10.1007/978-3-540-75225-7_5
A Kernel Two-Sample Testhttp://www.jmlr.org/papers/v13/gretton12a.html
Optimal kernel choice for large-scale two-sample testshttp://papers.nips.cc/paper/4727-optimal-kernel-choice-for-large-scale-two-sample-tests
Matlabhttps://github.com/lopezpaz/classifier_tests/tree/master/code/unit_test_mmd
Pythonhttps://github.com/jindongwang/transferlearning/tree/master/code/basic/mmd.py
Asymmetric Heterogeneous Transfer Learning: A Surveyhttps://arxiv.org/abs/1804.10834
Neural Style Transfer: A Reviewhttps://arxiv.org/abs/1705.04058
Deep Visual Domain Adaptation: A Surveyhttps://www.sciencedirect.com/science/article/pii/S0925231218306684
A survey on multi-task learninghttps://arxiv.org/abs/1707.08114
A survey on heterogeneous transfer learninghttps://link.springer.com/article/10.1186/s40537-017-0089-0
Cross-dataset recognition: a surveyhttps://arxiv.org/abs/1705.04396
A survey of transfer learninghttps://pan.baidu.com/s/1gfgXLXT
迁移学习研究进展https://pan.baidu.com/s/1bpautob
Visual Domain Adaptation: A Survey of Recent Advanceshttps://pan.baidu.com/s/1o8BR7Vc
Transfer Learning for Activity Recognition: A Surveyhttps://pan.baidu.com/s/1kVABOYr
Transfer Learning for Reinforcement Learning Domains: A Surveyhttps://pan.baidu.com/s/1slfr0w1
A Survey of Multi-source Domain Adaptationhttps://pan.baidu.com/s/1eSGREF4
https://github.com/BasicPower/transferlearning/tree/master#4代码
这里https://github.com/jindongwang/transferlearning/tree/master/code
HEREhttps://github.com/jindongwang/transferlearning/tree/master/code
https://github.com/BasicPower/transferlearning/tree/master#5迁移学习代表性研究学者
这里https://github.com/jindongwang/transferlearning/blob/master/doc/scholar_TL.md
herehttps://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/
Google scholarhttps://scholar.google.com/citations?user=P6WcnfkAAAAJ&hl=zh-CN
Wenyuan Daihttps://scholar.google.com.sg/citations?user=AGR9pP0AAAAJ&hl=zh-CN
Google scholarhttps://scholar.google.com.hk/citations?hl=zh-CN&user=AGR9pP0AAAAJ
Lixin Duanhttp://www.lxduan.info/
Google scholarhttps://scholar.google.com.hk/citations?user=inRIcS0AAAAJ&hl=zh-CN&oi=ao
Fuzhen Zhuanghttp://www.intsci.ac.cn/users/zhuangfuzhen/
Google scholarhttps://scholar.google.com/citations?user=klJBYrAAAAAJ&hl=zh-CN&oi=ao
Mingsheng Longhttp://ise.thss.tsinghua.edu.cn/~mlong/
Google scholarhttps://scholar.google.com/citations?view_op=search_authors&mauthors=mingsheng+long&hl=zh-CN&oi=ao
Qingyao Wuhttps://sites.google.com/site/qysite/
Google scholarhttps://scholar.google.com.hk/citations?user=n6e_2IgAAAAJ&hl=zh-CN&oi=ao
Weike Panhttps://sites.google.com/site/weikep/
Google Scholarhttps://scholar.google.com/citations?user=pC5Q26MAAAAJ&hl=en
Tongliang Liuhttp://ieeexplore.ieee.org/abstract/document/8259375/
Google scholarhttps://scholar.google.com.hk/citations?hl=zh-CN&user=EiLdZ_YAAAAJ
https://github.com/BasicPower/transferlearning/tree/master#6迁移学习相关的硕博士论文
Correlation Alignment for Domain Adaptationhttp://www.cs.uml.edu/~bsun/papers/baochen_phd_thesis.pdf
Kernel Methods for Unsupervised Domain Adaptationhttps://pan.baidu.com/s/1bpbawv9
迁移学习问题与方法研究http://ise.thss.tsinghua.edu.cn/~mlong/doc/phd-thesis-mingsheng-long.pdf
自适应行为识别中的迁移学习方法研究https://pan.baidu.com/s/1kVqYXnh
Learning based Activity Recognitionhttps://pan.baidu.com/s/1bp2K9HX
Learning with Limited Data in Sensor-based Human Behavior Predictionhttps://pan.baidu.com/s/1o8MbbBk
Feature-based Transfer Learning and Its Applicationshttps://pan.baidu.com/s/1bUqMfW
基于实例和特征的迁移学习算法研究https://pan.baidu.com/s/1i4Vyygd
完整版https://pan.baidu.com/s/1bqXEASn
https://github.com/BasicPower/transferlearning/tree/master#7公开数据集及实验结果
HEREhttps://github.com/jindongwang/transferlearning/blob/master/data
这里https://github.com/jindongwang/transferlearning/blob/master/data
https://github.com/BasicPower/transferlearning/tree/master#8比赛
Visual Domain Adaptation Challenge (VisDA)http://ai.bu.edu/visda-2018/
https://github.com/BasicPower/transferlearning/tree/master#其他
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/BasicPower/transferlearning/tree/master#contributing
UPLOADhttps://my.pcloud.com/#page=puplink&code=4e9Z0Vwpmfzvx0y2OqTTTMzkrRUz8q9V
DOWNLOADhttps://my.pcloud.com/publink/show?code=kZWtboZbDDVguCHGV49QkmlLliNPJRMHrFX
transferlearning.xyz/http://transferlearning.xyz/
Readme https://github.com/BasicPower/transferlearning/tree/master#readme-ov-file
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