This is a Pytorch implementation of Unsupervised domain adaptation by backpropagation (also know as DANN or RevGrad).

• Python 3.6
• Pytorch 0.4.0

First, you need download two datasets: source dataset mnist,

cd dataset
mkdir mnist
cd mnist
wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz

and target dataset mnist_m from pan.baidu.com or Google Drive

cd dataset
mkdir mnist_m
cd mnist_m
tar -zvxf mnist_m.tar.gz

Then, run main.py

On MNIST - MNIST_M, I run 100 epochs and get the following results, which is extremely high compared to the paper:

Reference

Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation. ICML 2015.

If you have any questions, please open an issue.