This code requires Python 3, and is implemented in Tensorflow.

Hopefully things should be fairly easy to run out of the box:

pip install -r requirements.txt
mkdir data snapshot
export PYTHONPATH="$PWD:$PYTHONPATH"
scripts/svhn-mnist.sh

The provided script does the following things:

• Train a base LeNet model on SVHN (downloading SVHN under data/svhn in the process)
• Use ADDA to adapt the SVHN model to MNIST (downloading MNIST under data/mnist in the process)
• Run an evaluation on MNIST using the source-only model (stored at snapshot/lenet_svhn)
• Run an evaluation on MNIST using the ADDA model (stored at snapshot/adda_lenet_svhn_mnist)

• Check scripts/svhn-mnist.sh for hyperparameters.
• The LeNet model definition is in adda/models/lenet.py.
• The model is annotated with data preprocessing info, which is used in the preprocessing function in adda/models/model.py.
• The main ADDA logic happens in tools/train_adda.py.
• The adversarial discriminator model definition is in adda/adversary.py.