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# PyTorch-DANN
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This repo is mostly based on https://github.com/wogong/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 3.8.5
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- PyTorch 1.6.0
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## Note
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Before running the training code, make sure that `DATASETDIR` environment variable is set to dataset directory.
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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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- `GTSRBmodel()`
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- `AlexModel`
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- not successful, mainly due to the pretrained model difference
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- `ResNet50`
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- Better and more stable results than AlexNet.
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## Result
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| | MNIST-MNISTM | SVHN-MNIST | SYNDIGITS-SVHN | SYNSIGNS-GTSRB |
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| :------------------: | :------------: | :--------: |:-------------: |:-------------: |
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| Source Only | 0.5225 | 0.5490 | 0.8674 | 0.7900 |
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| DANN(paper) | 0.7666 | 0.7385 | 0.9109 | 0.8865 |
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| This Repo Source Only| - | - | - | 0.9100 |
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| This Repo | 0.8400 | 0.7339 | 0.8200 | - |
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| | AMAZON-WEBVCAM | DSLR-WEBCAM | WEBCAM-DSLR |
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| :------------------: | :------------: |:-----------: |:----------: |
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| Source Only | 0.6420 | 0.9610 | 0.9780 |
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| DANN(paper) | 0.7300 | 0.9640 | 0.9920 |
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| This Repo Source Only| - | - | - |
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| This Repo | 0.6528 | - | - |
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## Credit
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- <https://github.com/wogong/pytorch-dann>
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- <https://github.com/fungtion/DANN>
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- <https://github.com/hehai131/DANN>
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- <https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py>
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- <https://github.com/corenel/torchsharp>
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- <https://github.com/corenel/pytorch-starter-kit>
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