A PyTorch implementation for Adversarial Discriminative Domain Adaptation.

• Python 3.6
• PyTorch 0.2.0

I only test on MNIST -> USPS, you can just run the following command:

python3 main.py

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 () ) ) 

Domain Adaptation does work (97% vs 83%).