figures added

README.md

@@ -1,2 +1,115 @@
-# StarGAN
-StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Tranlsation (PyTorch Implemenation)
+<p align="center"><img width="40%" src="png/logo.png" /></p>
+
+--------------------------------------------------------------------------------
+PyTorch implementation of [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://arxiv.org/abs/1711.09020). StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator. 
+
+<p align="center"><img width="100%" src="png/main.png" /></p>
+
+## Authors
+
+[Yunjey Choi](https://github.com/yunjey), [Minje Choi](https://github.com/mjc92), [Munyoung Kim](https://www.facebook.com/munyoung.kim.1291), [Jung-Woo Ha](https://www.facebook.com/jungwoo.ha.921), [Sung Kim](https://www.cse.ust.hk/~hunkim/), and [Jaegul Choo](https://sites.google.com/site/jaegulchoo/)    
+Korea Universitiy, Clova AI Research (NAVER), The College of New Jersey, HKUST  
+&nbsp;
+
+
+## Results
+
+### Facial Attribute Transfer on CelebA
+The images are generated by StarGAN trained on the CelebA dataset.
+<p align="center"><img width="100%" src="png/result_celeb1.png" /></p>
+
+### Facial Expression Synthesis on RaFD
+The images are generated by StarGAN trained on the RaFD dataset.
+<p align="center"><img width="100%" src="png/result_rafd.png" /></p>
+
+### Facial Expression Synthesis on CelebA
+The images are generated by StarGAN trained on both the CelebA and RaFD dataset.
+<p align="center"><img width="100%" src="png/result_celeb2.png" /></p>
+
+
+&nbsp;
+
+## Model Description
+### Training within a Single Dataset
+Overview of StarGAN, consisting of two modules, a discriminator <b>D</b> and a generator <b>G</b>. <b>(a)</b> <b>D</b> learns to distinguish between real and fake images and classify the real images to its corresponding domain. <b>(b)</b> <b>G</b> takes in as input both the image and target domain label and generates an fake image. The target domain label is spatially replicated and concatenated with the input image. <b>(c)</b> <b>G</b> tries to reconstruct the original image from the fake image given the original domain label. <b>(d)</b> <b>G</b> tries to generate images indistinguishable from real images and classifiable as target domain by <b>D</b>.
+<p align="center"><img width="100%" src="png/model.png" /></p>
+
+### Training with Multiple Datasets
+Overview of StarGAN when training with both CelebA and RaFD. <b>(a) ~ (d)</b> shows the training process using CelebA, and <b>(e) ~ (h)</b> shows the training process using RaFD. <b>(a), (e)</b> The discriminator <b>D</b> learns to distinguish between real and fake images and minimize the classification error only for the known label. <b>(b), (c), (f), (g)</b> When the mask vector (purple) is [1, 0], the generator <b>G</b> learns to focus on the CelebA label (yellow) and ignore the RaFD label (green) to perform image-to-image translation, and vice versa when the mask vector is [0, 1]. <b>(d), (h)</b> <b>G</b> tries to generate images that are both indistinguishable from real images and classifiable by <b>D</b> as belonging to the target domain.
+<p align="center"><img width="100%" src="png/model2.png" /></p>
+
+&nbsp;
+
+## Prerequisites
+* [Python 2.7 or 3.5+](https://www.continuum.io/downloads)
+* [PyTorch 0.2.0](http://pytorch.org/)
+* [TensorFlow 1.3+](https://www.tensorflow.org/) (optional for tensorboard)
+
+&nbsp;
+
+## Getting Started
+
+#### 1. Clone the repository
+```bash
+$ git clone https://github.com/yunjey/StarGAN.git
+$ cd StarGAN/
+```
+
+#### 2. Download the dataset
+##### (i) CelebA dataset
+```bash
+$ bash download.sh
+```
+
+##### (ii) RaFD dataset
+Because <b>RaFD</b> is not a public dataset, you must first request access to the dataset from [the Radboud Faces Database website](http://www.socsci.ru.nl:8180/RaFD2/RaFD?p=main). Then, you need to create the folder structure as decribed [here.](https://github.com/yunjey/StarGAN/blob/master/png/RaFD.md)
+
+#### 3. Train StarGAN
+##### (i) Training with CelebA
+
+```bash
+$ python main.py --mode='train' --dataset='CelebA' --c_dim=5 --image_size=128 --num_epochs=20 --num_epochs_decay=10
+```
+##### (ii) Training with RaFD
+
+```bash
+$ python main.py --mode='train' --dataset='RaFD' --c_dim=8 --image_size=128 --num_epochs=200 --num_epochs_decay=100
+```
+
+##### (iii) Training with CelebA+RaFD
+
+```bash
+$ python main.py --mode='train' --dataset='Both' --c_dim=5 --c2_dim=8 --image_size=256 --num_iters=200000 --num_iters_decay=100000
+```
+
+#### 4. Test StarGAN
+##### (i) Facial attribute transfer on CelebA
+```bash
+$ python main.py --mode='test' --dataset='CelebA' --c_dim=5 --image_size=256 --test_model=20_1000
+```
+
+##### (ii) Facial expression synthesis on RaFD
+```bash
+$ python main.py --mode='test' --dataset='RaFD' --c_dim=8 --image_size=256 --test_model=200_200
+```
+
+##### (iii) Facial expression synthesis on CelebA
+```bash
+$ python main.py --mode='test' --dataset='Both' --c_dim=5 --c2_dim=8 --image_size=256 --test_model=200000
+```
+
+&nbsp;
+
+## Citation
+```bash
+@article{choi2017unified,
+ title = {StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation},    
+ author = {Choi, Yunjey and Choi, Minje and Kim, Munyoung and Ha, Jung-Woo and Kim, Sunghun and Choo, Jaegul},
+ journal= {arXiv preprint arXiv:1711.09020},
+ Year = {2017}
+}
+```
+&nbsp;
+
+## Acknowledgement
+This work was mainly done while the first author did a research internship at <b>Clova AI Research, NAVER (CLAIR)</b>. We also thank all the researchers at CLAIR, especially Donghyun Kwak, for insightful discussions.

png/RaFD.md

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+# RaFD dataset guideline
+#### 1) Split the dataset into training and test sets (90\%/10\% for training and test, respectively).  
+#### 2) Crop all images to 256 x 256, where the faces are centered, and save them in the format shown below.  
+
+    data
+    └── RaFD
+        ├── train
+        |   ├── angry
+        |   |   ├── aaa.jpg  (name doesn't matter)
+        |   |   ├── bbb.jpg
+        |   |   └── ...
+        |   ├── happy
+        |   |   ├── ccc.jpg
+        |   |   ├── ddd.jpg
+        |   |   └── ...
+        |   ...
+        |
+        └── test
+            ├── angry
+            |   ├── eee.jpg
+            |   ├── fff.jpg
+            |   └── ...
+            ├── happy
+            |   ├── ggg.jpg
+            |   ├── iii.jpg
+            |   └── ...
+            ...
+
+