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A PyTorch implementation for paper Unsupervised Domain Adaptation by Backpropagation
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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 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

Credit