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This is a implementation of Domain-Adversarial Training of Neural Networks
with pytorch. This paper introduced a simple and effective method for accompli-
shing domian adaptation with SGD with a GRL(Gradient Reveral Layer). According
to this paper, domain classifier is used to decrease the H-divergence between
source domain distribution and target domain distribution. For the tensorflow
version, you can see tf-dann.

python3.6.2
pip install -r requirements.txt

In this work, MNIST and MNIST_M datasets are used in experiments. MNIST dataset
can be downloaded with torchvision.datasets. MINIST_M dataset can be downloa-
ded at Yaroslav Ganin's homepage. Then you can extract the file to your data dire-
ctory and run the preprocess.py to make the directory able to be used with
torchvision.datasets.ImageFolder:

python preprocess.py

You can run main.py to implements the MNSIT experiments for the paper with the
similar model and same paramenters.The paper's results and this work's results a-
re as follows:

Experiment on SVHN->MNIST is added in this project, but some bugs are not fixed.
The accuracies of source and target domains are not good at the same time.

Experiment on SynDig->SVHN is added.