Awesome
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%).