# Domain Separation Networks


## Introduction
This code is the code used for the "Domain Separation Networks" paper
by Bousmalis K., Trigeorgis G., et al. which was presented at NIPS 2016. The
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paper can be found here: https://arxiv.org/abs/1608.06019

## Contact
This code was open-sourced by Konstantinos Bousmalis (konstantinos@google.com, github:bousmalis)
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paper can be found here: https://arxiv.org/abs/1608.06019.

## Contact
This code was open-sourced by [Konstantinos Bousmalis](https://github.com/bousmalis) (konstantinos@google.com).
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## Installation
You will need to have the following installed on your machine before trying out the DSN code.

*  Tensorflow: https://www.tensorflow.org/install/
*  Bazel: https://bazel.build/

## Important Note
Although we are making the code available, you are only able to use the MNIST
provider for now. We will soon provide a script to download and convert MNIST-M
as well. Check back here in a few weeks or wait for a relevant announcement from
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Twitter @bousmalis.
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[@bousmalis](https://twitter.com/bousmalis).
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## Running the code for adapting MNIST to MNIST-M
In order to run the MNIST to MNIST-M experiments with DANNs and/or DANNs with
domain separation (DSNs) you will need to set the directory you used to download
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MNIST and MNIST-M:\
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MNIST and MNIST-M:
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```
$ export DSN_DATA_DIR=/your/dir
```

Then you need to build the binaries with Bazel:

```
$ bazel build -c opt domain_adaptation/domain_separation/...
```

Add models and models/slim to your `$PYTHONPATH`:

```
$ export PYTHONPATH=$PYTHONPATH:$PWD:$PWD/slim
```

You can then train with the following command:

```
$ ./bazel-bin/domain_adaptation/domain_separation/dsn_train  \
      --similarity_loss=dann_loss  \
      --basic_tower=dann_mnist  \
      --source_dataset=mnist  \
      --target_dataset=mnist_m  \
      --learning_rate=0.0117249  \
      --gamma_weight=0.251175  \
      --weight_decay=1e-6  \
      --layers_to_regularize=fc3  \
      --nouse_separation  \
      --master=""  \
      --dataset_dir=${DSN_DATA_DIR}  \
      -v --use_logging
```

Evaluation can be invoked with the following command:

```
$ ./bazel-bin/domain_adaptation/domain_separation/dsn_eval  \
    -v --dataset mnist_m --split test --num_examples=9001  \
    --dataset_dir=${DSN_DATA_DIR}
```