PyTorch-ADDA

A PyTorch implementation for Adversarial Discriminative Domain Adaptation.

Environment

• Python 3.6
• PyTorch 0.2.0

Usage

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

python3 main.py

Network

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 ()
    )
  )
  

Result

	MNIST (Source)	USPS (Target)
Source Encoder + Source Classifier	99.140000%	83.978495%
Target Encoder + Source Classifier		97.634409%

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