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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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## Note
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- `Config()` 为针对特定任务的配置参数。
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- `MNISTmodel()` 完全按照论文中的结构，但是 feature 部分添加了 `Dropout2d()`，实验发现是否添加 `Dropout2d()` 对于最后的性能影响很大。最后实验重现结果高于论文，因为使用了额外的技巧，这里还有值得探究的地方。
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- `SVHNmodel()` 无法理解论文中提出的结构，为自定义结构。最后实验重现结果完美。
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- MNIST-MNISTM: `python mnist_mnistm.py`
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- SVHN-MNIST: `python svhn_mnist.py`
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- Amazon-Webcam: `python office.py` 没有复现成功
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## Result
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| | MNIST-MNISTM | SVHN-MNIST | Amazon-Webcam |Amazon-Webcam10 |
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| :------------------: | :------------: | :--------: | :-----------: |:-------------: |
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| Source Only | 0.5225 | 0.5490 | 0.6420 | 0. |
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| DANN(paper) | 0.7666 | 0.7385 | 0.7300 | 0. |
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| This Repo Source Only| - | - | - | 0. |
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| This Repo | 0.8400 | 0.7339 | 0.6528 | 0. |
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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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