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# PyTorch-DANN
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A pytorch implementation for paper *[Unsupervised Domain Adaptation by Backpropagation](http://sites.skoltech.ru/compvision/projects/grl/)*
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InProceedings (icml2015-ganin15)
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Ganin, Y. & Lempitsky, V.
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Unsupervised Domain Adaptation by Backpropagation
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Proceedings of the 32nd International Conference on Machine Learning, 2015
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## Environment
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- Python 2.7/3.6
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- PyTorch 0.3.1post2
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## Result
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results of the default `params.py`
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| | SVHN (Source) | MNIST (Target)|
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| :--------------------------------: | :------------: | :-----------: |
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| Source Classifier | 92.92% | 68.66% |
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| DANN | | ----% |
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## Other implementations
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- authors(caffe) <https://github.com/ddtm/caffe>
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- TensorFlow, <https://github.com/pumpikano/tf-dann>
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- Theano, <https://github.com/shucunt/domain_adaptation>
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- PyTorch, <https://github.com/fungtion/DANN>
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## Credit
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- <https://github.com/fungtion/DANN>
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- <https://github.com/corenel/torchsharp>
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- <https://github.com/corenel/pytorch-starter-kit>
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