Domain adversarial neural network (DANN/RevGrad)

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

Requirements

Python 3.6
Pytorch 0.4.0

Usage

Dataset

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

Training and testing

Then, run main.py

Results

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.