A PyTorch implementation for Adversarial Discriminative Domain Adaptation.

• Python 3.6
• PyTorch 0.3.1post2

In this experiment, I use three types of network. They are very simple.

• LeNet encoder

  LeNetEncoder (
    (encoder): Sequential (
      (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
      (1): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
      (2): ReLU ()
      (3): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))
      (4): Dropout2d (p=0.5)
      (5): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
      (6): ReLU ()
    )
    (fc1): Linear (800 -> 500)
  )
  

• LeNet classifier

  LeNetClassifier (
    (fc2): Linear (500 -> 10)
  )
  

• Discriminator

  Discriminator (
    (layer): Sequential (
      (0): Linear (500 -> 500)
      (1): ReLU ()
      (2): Linear (500 -> 500)
      (3): ReLU ()
      (4): Linear (500 -> 2)
      (5): LogSoftmax ()
    )
  )
  

1. mnist-usps 实验使用的是部分的样本（2000/1800），注意修改代码。实验设置参考文献 \cite{long2013transfer}. sampling 2000 images from MNIST and 1800 from USPS
2. svhn-mnist 重现，不使用 BN 无法收敛，同样的结论来自https://github.com/aabbas90/ADDA-PyTorch

• authors(tensorflow) https://github.com/erictzeng/adda
• pytorch https://github.com/corenel/pytorch-adda
• pytorch forks https://github.com/Zzlongjuanfeng/pytorch-adda
• pytorch https://github.com/aabbas90/ADDA-PyTorch