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wogong
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core | 5 years ago | |
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README.md
PyTorch-DANN
A PyTorch implementation for paper Unsupervised Domain Adaptation by Backpropagation
InProceedings (icml2015-ganin15)
Ganin, Y. & Lempitsky, V.
Unsupervised Domain Adaptation by Backpropagation
Proceedings of the 32nd International Conference on Machine Learning, 2015
Environment
- Python 3.6
- PyTorch 1.0
Note
Config()
为针对特定任务的配置参数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。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。- MNIST-MNISTM:
python mnist_mnistm.py
- SVHN-MNIST:
python svhn_mnist.py
- Amazon-Webcam:
python office.py
由于预训练网络的问题,无法复现
Result
MNIST-MNISTM | SVHN-MNIST | Amazon-Webcam | Amazon-Webcam10 | |
---|---|---|---|---|
Source Only | 0.5225 | 0.5490 | 0.6420 | 0. |
DANN(paper) | 0.7666 | 0.7385 | 0.7300 | 0. |
This Repo Source Only | - | - | - | 0. |
This Repo | 0.8400 | 0.7339 | 0.6528 | 0. |