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README.md

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-# SCGAN
+# SCGAN
+
+Code for reproducing experiments in "SCGAN: Interpretable Representation Learning
+by Adding Similarity Constraint on Generative
+Adversarial Nets"
+
+## Prerequisites
+
+* Python
+* Numpy
+* Matplotlib
+* Tensorflow >= 1.5
+
+## Preparing datasets
+
+The tree of directory is listed as follow:
+- DATA_DIR
+    + mnist
+    + fashion-mnist
+    + svhn
+    + cifar10
+    + celeba
+
+The `DATA_DIR` is root directory of these datasets, you can set it any path you want, e.g. `/home/username`. Then you should download all datasets.
+
+1. Download mnist dataset from [MNIST](http://yann.lecun.com/exdb/mnist), and put `train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz, t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz` into `DATA_DIR/mnist`.
+
+2. Download fashion-mnist dataset from [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist), and put `train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz, t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz` into `DATA_DIR/fashion-mnist`.
+
+3. Download svhn dataset from [SVHN](http://ufldl.stanford.edu/housenumbers), and put `train_32x32.mat` into `DATA_DIR/svhn`.
+
+4. Download cifar10 dataset from [CIFAR](http://www.cs.toronto.edu/~kriz/cifar.html), extract `cifar-10-batches-py` from `cifar-10-python.tar.gz`, and put `cifar-10-batches-py` into `DATA_DIR/cifar10`.
+
+5. Download celeba dataset from `...........`, and put `celeba_0, celeba_1, celeba_2, celeba_3`(We have converted it to tfrecords) into `DATA_DIR/celeba`.
+
+## MNIST & Fashion-MNIST
+
+### Train on MNIST
+
+```
+python cgan_mnist.py --data_dir=DATA_DIR
+python infogan_mnist.py --data_dir=DATA_DIR
+python scgan_mnist.py --data_dir=DATA_DIR
+```
+
+### Test on MNIST
+
+It will create `*.npy` which contains 10000 synthetic images.
+
+```
+python cgan_mnist.py --train=False --data_dir=DATA_DIR
+python scgan_mnist.py --train=False --data_dir=DATA_DIR
+python infogan_mnist.py --train=False --data_dir=DATA_DIR
+```
+
+
+### Train on Fashion-MNIST
+
+```
+python cgan_mnist.py --dataset_type=fashion-mnist --data_dir=DATA_DIR
+python infogan_mnist.py --dataset_type=fashion-mnist --data_dir=DATA_DIR
+python scgan_mnist.py --dataset_type=fashion-mnist --data_dir=DATA_DIR
+```
+
+
+### Test on Fashion-MNIST
+
+It will create `*.npy` which contains 10000 synthetic images.
+
+```
+python cgan_mnist.py --train=False --dataset_type=fashion-mnist --data_dir=DATA_DIR
+python infogan_mnist.py --train=False --dataset_type=fashion-mnist --data_dir=DATA_DIR
+python scgan_mnist.py --train=False --dataset_type=fashion-mnist --data_dir=DATA_DIR
+```
+
+On MNIST and Fashion-MNIST, we find categorical conditional variables capture class labels, e.g., digit type, clothing type.
+
+<div align=center>
+    <img src="images/MNIST.png"/>
+</div>
+
+### Gaussian Parzen Window
+
+```
+python gaussian_parzen_window.py --data_dir DATA_DIR/mnist --gen_data_path result/cgan_mnist/cgan_mnist.npy --file result/cgan_mnist/cgan_mnist.txt
+python gaussian_parzen_window.py --data_dir DATA_DIR/mnist --gen_data_path result/scgan_mnist/scgan_mnist.npy --file result/scgan_mnist/scgan_mnist.txt
+python gaussian_parzen_window.py --data_dir DATA_DIR/mnist --gen_data_path result/infogan_mnist/infogan_mnist.npy --file result/infogan_mnist/infogan_mnist.txt
+python gaussian_parzen_window.py --data_dir DATA_DIR/fashion-mnist --gen_data_path result/cgan_fashion-mnist/cgan_fashion-mnist.npy --file result/cgan_fashion-mnist/cgan_fashion-mnist.txt
+python gaussian_parzen_window.py --data_dir DATA_DIR/fashion-mnist --gen_data_path result/scgan_fashion-mnist/scgan_fashion-mnist.npy --file result/scgan_fashion-mnist/scgan_fashion-mnist.txt
+python gaussian_parzen_window.py --data_dir DATA_DIR/fashion-mnist --gen_data_path result/infogan_fashion-mnist/infogan_fashion-mnist.npy --file result/infogan_fashion-mnist/infogan_fashion-mnist.txt
+```
+
+Gaussian Parzen window-based log-likelihood estimates for MNIST and Fashion-MNIST.
+
+<table align="center">
+    <thead>
+        <tr>
+            <th align="left">Model</th>
+            <th align="left">MNIST</th>
+            <th align="left">Fashion-MNIST</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td align="left">CGAN</td>
+            <td align="left">228.1 &plusmn; 2.2</td>
+            <td align="left">312.7 &plusmn; 2</td>
+        </tr>
+        <tr>
+            <td align="left">InfoGAN</td>
+            <td align="left">231 &plusmn; 2.2</td>
+            <td align="left">320.2 &plusmn; 2</td>
+        </tr>
+        <tr>
+            <th align="left">SCGAN</th>
+            <th align="left">233.6 &plusmn; 2.2</th>
+            <th align="left">321.7 &plusmn; 2</th>
+        </tr>
+    </tbody>
+</table>
+
+
+## SVHN & CIFAR10
+
+### Train on SVHN
+
+```
+python scgan_svhn.py --data_dir=DATA_DIR
+```
+
+### Test on SVHN
+
+Fix one conditional variable, and vary the other conditional variable.
+
+```
+python scgan_svhn.py --train=False --data_dir=DATA_DIR --con_dim=0
+python scgan_svhn.py --train=False --data_dir=DATA_DIR --con_dim=1
+```
+
+We select some good-quality synthetic images. Continuous variables capture variation on lighting and digit types.
+<div align=center>
+    <img src="images/SVHN.png"/>
+</div>
+
+
+### Train on CIFAR10
+
+```
+python scgan_cifar.py --data_dir=DATA_DIR
+```
+
+### Test on CIFAR10
+
+Fix one conditional variable, and vary the other conditional variable.
+
+```
+python scgan_cifar.py --train=False --data_dir=DATA_DIR --con_dim=0
+python scgan_cifar.py --train=False --data_dir=DATA_DIR --con_dim=1
+```
+
+We select some good-quality synthetic images. Continuous variables capture variation on object size and colour.
+<div align=center>
+    <img src="images/CIFAR10.png"/>
+</div>
+
+## CelebA
+
+### Train on CelebA
+
+```
+python scgan_celeba.py --data_dir=DATA_DIR
+```
+
+### Test on CelebA
+
+```
+python scgan_celeba.py --train=False --data_dir=DATA_DIR
+```
+
+We select some good-quality synthetic images. Continuous variables capture variation on pose, glasses, hair and sex.
+<div align=center>
+    <img src="images/CELEBA.png"/>
+</div>
+
+## Citing SCGAN
+If you use SCGAN in your research, we would appreciate references to the following paper:
+
+Biblatex entry: