https://github.com/thuml/Xlearn

Transfer Learning Library
https://github.com/thuml/Xlearn

deep-learning transfer-learning

Last synced: 2 months ago
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Transfer Learning Library

• Host: GitHub
• URL: https://github.com/thuml/Xlearn
• Owner: thuml
• Created: 2017-09-25T10:23:31.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-04-09T12:31:53.000Z (about 3 years ago)
• Last Synced: 2024-01-25T19:08:16.378Z (5 months ago)
• Topics: deep-learning, transfer-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 8.66 MB
• Stars: 454
• Watchers: 16
• Forks: 157
• Open Issues: 21
• Metadata Files:
  • Readme: README.md

Lists

• Awesome-pytorch-list - Xlearn
• Awesome-pytorch-list-CNVersion - Xlearn
• awesome-domain-adaptation - Xlearn:Transfer Learning Library

README

        
# Xlearn (Obsolete, upgraded to https://github.com/thuml/Transfer-Learning-Library)

Transfer Learning Library

This is the transfer learning library for the following paper:

### Learning Transferable Features with Deep Adaptation Networks

### Unsupervised Domain Adaptation with Residual Transfer Networks

### Deep Transfer Learning with Joint Adaptation Networks

The tensorflow versions are under developing.

## Citation

If you use this code for your research, please consider citing:

```

    @inproceedings{DBLP:conf/icml/LongC0J15,

      author    = {Mingsheng Long and

                   Yue Cao and

                   Jianmin Wang and

                   Michael I. Jordan},

      title     = {Learning Transferable Features with Deep Adaptation Networks},

      booktitle = {Proceedings of the 32nd International Conference on Machine Learning,

                   {ICML} 2015, Lille, France, 6-11 July 2015},

      pages     = {97--105},

      year      = {2015},

      crossref  = {DBLP:conf/icml/2015},

      url       = {http://jmlr.org/proceedings/papers/v37/long15.html},

      timestamp = {Tue, 12 Jul 2016 21:51:15 +0200},

      biburl    = {http://dblp2.uni-trier.de/rec/bib/conf/icml/LongC0J15},

      bibsource = {dblp computer science bibliography, http://dblp.org}

    }

    

    @inproceedings{DBLP:conf/nips/LongZ0J16,

      author    = {Mingsheng Long and

                   Han Zhu and

                   Jianmin Wang and

                   Michael I. Jordan},

      title     = {Unsupervised Domain Adaptation with Residual Transfer Networks},

      booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference

                   on Neural Information Processing Systems 2016, December 5-10, 2016,

                   Barcelona, Spain},

      pages     = {136--144},

      year      = {2016},

      crossref  = {DBLP:conf/nips/2016},

      url       = {http://papers.nips.cc/paper/6110-unsupervised-domain-adaptation-with-residual-transfer-networks},

      timestamp = {Fri, 16 Dec 2016 19:45:58 +0100},

      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/nips/LongZ0J16},

      bibsource = {dblp computer science bibliography, http://dblp.org}

    }

    

    @inproceedings{DBLP:conf/icml/LongZ0J17,

      author    = {Mingsheng Long and

                   Han Zhu and

                   Jianmin Wang and

                   Michael I. Jordan},

      title     = {Deep Transfer Learning with Joint Adaptation Networks},

      booktitle = {Proceedings of the 34th International Conference on Machine Learning,

               {ICML} 2017, Sydney, NSW, Australia, 6-11 August 2017},

      pages     = {2208--2217},

      year      = {2017},

      crossref  = {DBLP:conf/icml/2017},

      url       = {http://proceedings.mlr.press/v70/long17a.html},

      timestamp = {Tue, 25 Jul 2017 17:27:57 +0200},

      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/icml/LongZ0J17},

      bibsource = {dblp computer science bibliography, http://dblp.org}

    }

```

## Contact

If you have any problem about our code, feel free to contact 

- [email protected]

- [email protected]

or describe your problem in Issues.