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wogong 7 years ago
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README.md

@ -12,14 +12,24 @@ A pytorch implementation for paper *[Unsupervised Domain Adaptation by Backpropa
- 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
`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
results of the default `params.py`
| | MNIST-MNISTM | SVHN-MNIST |
| :-------------: | :------------: | :--------: |
| Source Only | 0.5225 | 0.5490 |
| DANN | 0.7666 | 0.7385 |
| This Repo | 0.8400 | 0.7339 |
| | SVHN (Source) | MNIST (Target)|
| :--------------------------------: | :------------: | :-----------: |
| Source Classifier | 92.92% | 68.66% |
| DANN | | ----% |
- MNIST-MNISTM: `python mnist_mnistm.py`
- SVHN-MNIST: `python svhn_mnist.py`
## Other implementations
@ -27,6 +37,8 @@ results of the default `params.py`
- 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>
## Credit

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