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	Amazon-Webcam
Source Only	0.5225	0.5490	0.6420
DANN	0.7666	0.7385	0.7300
This Repo	0.8400	0.7339	0.6214

• MNIST-MNISTM: python mnist_mnistm.py
• SVHN-MNIST: python svhn_mnist.py
• Amazon-Webcam: 没有复现成功

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

Credit

• https://github.com/fungtion/DANN
• https://github.com/corenel/torchsharp
• https://github.com/corenel/pytorch-starter-kit