About Deep Domain Adaptation Networks (ddan)

Deep domain adaptation networks (DDAN) is a Python library for domain adapation written in TensorFlow.

Implemented models

The following neural network models are implemented in ddan:

• Adaptive Batch Normalization (AdaBN, paper here: https://arxiv.org/abs/1603.04779)
• Domain-adversarial Networks (Gradient reversal layer, paper here: https://arxiv.org/abs/1505.07818 and https://arxiv.org/abs/1409.7495)
• Deep-domain Confusion Network (siamese network with MMD loss, paper here: https://arxiv.org/abs/1412.3474)
• Fine-tuning Network

Installation

git clone https://github.com/erlendd/ddan.git
cd ddan
sudo pip install .
*OR*

   sudo python setup.py install

Usage

Full working examples can be found in the examples/ directory.

import ddan

model = ddan.DANNModel(epochs=1000, batch_size=64)
# Xs, ys are for the source domain, Xt, (yt) are for the target domain
model.fit(Xs, ys, Xt)
# Obtain predictions from trained model
yprobs = model.predict_proba(Xval)