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wogong
f41466b2d5
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7 years ago | |
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core | 7 years ago | |
datasets | 7 years ago | |
models | 7 years ago | |
.gitignore | 7 years ago | |
LICENSE | 7 years ago | |
README.md | 7 years ago | |
main_office.py | 7 years ago | |
mnist_mnistm.py | 7 years ago | |
svhn_mnist.py | 7 years ago | |
utils.py | 7 years ago |
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 2.7/3.6
- PyTorch 0.3.1post2
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。
Result
MNIST-MNISTM | SVHN-MNIST | |
---|---|---|
Source Only | 0.5225 | 0.5490 |
DANN | 0.7666 | 0.7385 |
This Repo | 0.8400 | 0.7339 |
- MNIST-MNISTM:
python mnist_mnistm.py
- SVHN-MNIST:
python svhn_mnist.py
Other implementations
- authors(caffe) https://github.com/ddtm/caffe
- TensorFlow, https://github.com/pumpikano/tf-dann
- Theano, https://github.com/shucunt/domain_adaptation
- PyTorch, https://github.com/fungtion/DANN
- numpy, https://github.com/GRAAL-Research/domain_adversarial_neural_network
- lua, https://github.com/gmarceaucaron/dann