README.md Updates

domain_adaptation/domain_separation/README.md

 # Domain Seperation 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
+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)
+
+## 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
+Twitter @bousmalis.
+
+## 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
+MNIST and MNIST-M:\
+$ export DSN_DATA_DIR=/your/dir
+
+Then you need to build the binaries with Bazel:
+
+$ bazel build -c opt domain_adaptation/domain_separation/...
+
+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}
+# Domain Seperation 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
@@ -17,7 +71,7 @@ You will need to have the following installed on your machine before trying out
 ## 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
-MNIST and MNIST-M:
+MNIST and MNIST-M:\
 $ export DSN_DATA_DIR=/your/dir
 
 Then you need to build the binaries with Bazel:
@@ -26,7 +80,7 @@ $ bazel build -c opt domain_adaptation/domain_separation/...
 
 Add models and models/slim to your $PYTHONPATH:
 
-$ export PYTHONPATH=$PYTHONPATH:$PWD/slim
+$ export PYTHONPATH=$PYTHONPATH:$PWD/slim\
 $ export PYTHONPATH=$PYTHONPATH:$PWD
 
 You can then train with the following command: