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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 3.6
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- PyTorch 1.0
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## Note
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- `MNISTmodel()`
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- basically the same network structure as proposed in the paper, expect for adding dropout layer in feature extractor
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- large gap exsits between with and w/o dropout layer
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- better result than paper
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- `SVHNmodel()`
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- network structure proposed in the paper may be wrong for both 32x32 and 28x28 inputs
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- change last conv layer's filter to 4x4, get similar(actually higher) result
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- `AlexModel`
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- not successful, mainly due to the preprain model difference
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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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