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Title: GitHub - minghao2016/transferlearning: Everything about Transfer Learning and Domain Adaptation--迁移学习 · GitHub

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Contributinghttps://github.com/minghao2016/transferlearning
MIT licensehttps://github.com/minghao2016/transferlearning
https://github.com/minghao2016/transferlearning#迁移学习-transfer-learning
https://opensource.org/licenses/MIT
迁移学习 Transfer Learninghttps://github.com/minghao2016/transferlearning#%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0-transfer-learning
0.Latest Publications (最新论文)https://github.com/minghao2016/transferlearning#0latest-publications-%E6%9C%80%E6%96%B0%E8%AE%BA%E6%96%87
1.Introduction and Tutorials (简介与教程)https://github.com/minghao2016/transferlearning#1introduction-and-tutorials-%E7%AE%80%E4%BB%8B%E4%B8%8E%E6%95%99%E7%A8%8B
2.Transfer Learning Areas and Papers (研究领域与相关论文)https://github.com/minghao2016/transferlearning#2transfer-learning-areas-and-papers-%E7%A0%94%E7%A9%B6%E9%A2%86%E5%9F%9F%E4%B8%8E%E7%9B%B8%E5%85%B3%E8%AE%BA%E6%96%87
3.Theory and Survey (理论与综述)https://github.com/minghao2016/transferlearning#3theory-and-survey-%E7%90%86%E8%AE%BA%E4%B8%8E%E7%BB%BC%E8%BF%B0
4.Code (代码)https://github.com/minghao2016/transferlearning#4code-%E4%BB%A3%E7%A0%81
5.Transfer Learning Scholars (著名学者)https://github.com/minghao2016/transferlearning#5transfer-learning-scholars-%E8%91%97%E5%90%8D%E5%AD%A6%E8%80%85
6.Transfer Learning Thesis (硕博士论文)https://github.com/minghao2016/transferlearning#6transfer-learning-thesis-%E7%A1%95%E5%8D%9A%E5%A3%AB%E8%AE%BA%E6%96%87
7.Datasets and Benchmarks (数据集与评测结果)https://github.com/minghao2016/transferlearning#7datasets-and-benchmarks-%E6%95%B0%E6%8D%AE%E9%9B%86%E4%B8%8E%E8%AF%84%E6%B5%8B%E7%BB%93%E6%9E%9C
8.Transfer Learning Challenges (迁移学习比赛)https://github.com/minghao2016/transferlearning#8transfer-learning-challenges-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%AF%94%E8%B5%9B
Applications (迁移学习应用)https://github.com/minghao2016/transferlearning#applications-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E5%BA%94%E7%94%A8
Other Resources (其他资源)https://github.com/minghao2016/transferlearning#other-resources-%E5%85%B6%E4%BB%96%E8%B5%84%E6%BA%90
Contributing (欢迎参与贡献)https://github.com/minghao2016/transferlearning#contributing-%E6%AC%A2%E8%BF%8E%E5%8F%82%E4%B8%8E%E8%B4%A1%E7%8C%AE
行为识别https://github.com/jindongwang/activityrecognition
机器学习https://github.com/jindongwang/MachineLearning
https://github.com/minghao2016/transferlearning#0latest-publications-最新论文
迁移学习文章汇总 Awesome transfer learning papershttps://github.com/jindongwang/transferlearning/tree/master/doc/awesome_paper.md
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learninghttps://arxiv.org/abs/1902.11128
One-Shot Federated Learninghttps://arxiv.org/abs/1902.11175
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptationhttps://arxiv.org/abs/1810.01545
Transfer Learning for Prosthetics Using Imitation Learninghttps://arxiv.org/abs/1901.04772
A Principled Approach for Learning Task Similarity in Multitask Learninghttps://arxiv.org/abs/1903.09109
Learning to Augment Synthetic Images for Sim2Real Policy Transferhttps://arxiv.org/abs/1903.07740
Transfer Adaptation Learning: A Decade Surveyhttps://arxiv.org/abs/1903.04687
Let's Transfer Transformations of Shared Semantic Representationshttps://arxiv.org/abs/1903.00793
Zero-Shot Task Transferhttps://arxiv.org/abs/1903.01092
更多 More...https://github.com/jindongwang/transferlearning/tree/master/doc/awesome_paper.md
https://github.com/minghao2016/transferlearning#1introduction-and-tutorials-简介与教程
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
更多 More...https://github.com/jindongwang/transferlearning/tree/master/code
https://github.com/minghao2016/transferlearning#2transfer-learning-areas-and-papers-研究领域与相关论文
General Transfer Learning (普通迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#general-transfer-learning-%E6%99%AE%E9%80%9A%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Theory (理论)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#theory-%E7%90%86%E8%AE%BA
Others (其他)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#others-%E5%85%B6%E4%BB%96
Domain Adaptation (领域自适应)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#domain-adaptation-%E9%A2%86%E5%9F%9F%E8%87%AA%E9%80%82%E5%BA%94
Traditional Methods (传统迁移方法)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#traditional-methods-%E4%BC%A0%E7%BB%9F%E8%BF%81%E7%A7%BB%E6%96%B9%E6%B3%95
Deep / Adversarial Methods (深度/对抗迁移方法)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#deep--adversarial-methods-%E6%B7%B1%E5%BA%A6%E5%AF%B9%E6%8A%97%E8%BF%81%E7%A7%BB%E6%96%B9%E6%B3%95
Domain Generalizationhttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#domain-generalization
Multi-source Transfer Learning (多源迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#multi-source-transfer-learning-%E5%A4%9A%E6%BA%90%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Heterogeneous Transfer Learning (异构迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#heterogeneous-transfer-learning-%E5%BC%82%E6%9E%84%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Online Transfer Learning (在线迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#online-transfer-learning-%E5%9C%A8%E7%BA%BF%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Zero-shot / Few-shot Learninghttps://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#zero-shot--few-shot-learning
Deep Transfer Learning (深度迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#deep-transfer-learning-%E6%B7%B1%E5%BA%A6%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Non-Adversarial Transfer Learning (非对抗深度迁移)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#non-adversarial-transfer-learning-%E9%9D%9E%E5%AF%B9%E6%8A%97%E6%B7%B1%E5%BA%A6%E8%BF%81%E7%A7%BB
Deep Adversarial Transfer Learning (对抗迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#deep-adversarial-transfer-learning-%E5%AF%B9%E6%8A%97%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Multi-task Learning (多任务学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#multi-task-learning-%E5%A4%9A%E4%BB%BB%E5%8A%A1%E5%AD%A6%E4%B9%A0
Transfer Reinforcement Learning (强化迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#transfer-reinforcement-learning-%E5%BC%BA%E5%8C%96%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Transfer Metric Learning (迁移度量学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#transfer-metric-learning-%E8%BF%81%E7%A7%BB%E5%BA%A6%E9%87%8F%E5%AD%A6%E4%B9%A0
Transitive Transfer Learning (传递迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#transitive-transfer-learning-%E4%BC%A0%E9%80%92%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Lifelong Learning (终身迁移学习)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#lifelong-learning-%E7%BB%88%E8%BA%AB%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
Negative Transfer (负迁移)https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#negative-transfer-%E8%B4%9F%E8%BF%81%E7%A7%BB
Transfer Learning Applications (应用)https://github.com/jindongwang/transferlearning/blob/master/doc/transfer_learning_application.md
Paperweeklyhttp://www.paperweekly.site/collections/231/papers
https://github.com/minghao2016/transferlearning#3theory-and-survey-理论与综述
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
Transfer Adaptation Learning: A Decade Surveyhttps://arxiv.org/abs/1903.04687
Transfer Metric Learning: Algorithms, Applications and Outlookshttps://arxiv.org/abs/1810.03944
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/minghao2016/transferlearning#4code-代码
这里https://github.com/jindongwang/transferlearning/tree/master/code
HEREhttps://github.com/jindongwang/transferlearning/tree/master/code
https://github.com/minghao2016/transferlearning#5transfer-learning-scholars-著名学者
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
Boqing Gonghttp://boqinggong.info/index.html
Google scholarhttps://scholar.google.com/citations?user=lv9ZeVUAAAAJ&hl=en
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
Tatiana Tommasihttp://tatianatommasi.wixsite.com/tatianatommasi/3
https://github.com/minghao2016/transferlearning#6transfer-learning-thesis-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/minghao2016/transferlearning#7datasets-and-benchmarks-数据集与评测结果
HEREhttps://github.com/jindongwang/transferlearning/blob/master/data
这里https://github.com/jindongwang/transferlearning/blob/master/data
https://github.com/minghao2016/transferlearning#8transfer-learning-challenges-迁移学习比赛
Visual Domain Adaptation Challenge (VisDA)http://ai.bu.edu/visda-2018/
https://github.com/minghao2016/transferlearning#applications-迁移学习应用
HEREhttps://github.com/jindongwang/transferlearning/blob/master/doc/transfer_learning_application.md
这里https://github.com/jindongwang/transferlearning/blob/master/doc/transfer_learning_application.md
https://github.com/minghao2016/transferlearning#other-resources-其他资源
Transfer Learning for Multimedia Applications(A Special Issue on Multimedia Tools and Applications (MTAP))https://lijin118.github.io/mtap/
A semi-supervised domain adaptation libraryhttps://domainadaptation.org
https://github.com/minghao2016/transferlearning#contributing-欢迎参与贡献
HEREhttps://github.com/jindongwang/transferlearning/blob/master/CONTRIBUTING.md
transferlearning.xyz/http://transferlearning.xyz/
Readme https://github.com/minghao2016/transferlearning#readme-ov-file
MIT license https://github.com/minghao2016/transferlearning#MIT-1-ov-file
Contributing https://github.com/minghao2016/transferlearning#contributing-ov-file
Please reload this pagehttps://github.com/minghao2016/transferlearning
Activityhttps://github.com/minghao2016/transferlearning/activity
0 forkshttps://github.com/minghao2016/transferlearning/forks
Report repository https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fminghao2016%2Ftransferlearning&report=minghao2016+%28user%29
Releaseshttps://github.com/minghao2016/transferlearning/releases
Packages 0https://github.com/users/minghao2016/packages?repo_name=transferlearning
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Contributorshttps://github.com/minghao2016/transferlearning/graphs/contributors
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