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wogong
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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.py | 7 years ago | |
main_office.py | 7 years ago | |
params.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
Result
results of the default params.py
SVHN (Source) | MNIST (Target) | |
---|---|---|
Source Classifier | 92.92% | 68.66% |
DANN | ----% |
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