SemiStarGAN

SemiStarGAN: Semi-Supervised Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Source code of ACCV2018 paper.

Citation

@inproceedings{Hsu:ACCV2018,
  year = { 2018 },
  month = { Nov },
  booktitle = { ACCV },
  title = { SemiStarGAN: SemiSupervised Generative Adversarial Networks for Multi-Domain Image-to-Image Translation },
  author = { Shu-Yu Hsu and Chih-Yuan Yang and Chi-Chia Huang and Jane Yung-jen Hsu },
}

Prerequisites

• Python 3.5
• Tensorflow 1.3.0
• Scipy
• tqdm

Usage

Data Preparation

Download CelebA dataset with:

$ python download.py

The images will be placed under data/celebA/images. Split the images into three folders: train, test and unlabel. Note the label file (ex: list_attr_celeba.txt for CelebA) should place under the same folder as train, test, unlabel folders are place.

Training

To train a model with SemiStarGAN:

$ python main.py --phase=train --image_size=128 --batch_size=16 --data_dir=your_dir

If your task is translating among multi-label attributes, use sigmoid instead of softmax

$ python main.py --phase=train --image_size=128 --batch_size=16 --data_dir=your_dir --c_method=Sigmoid

Test

To generate translated images for examining (testing) the model:

$ python main.py --phase=test_all --image_size=128 --data_dir=your_dir

There are other 2 phases to choose for testing:

• --phase=test, assign a binary attributes by giving --binary_attrs=100 (black hair in the case of hair colors)
• --phase=aux_test, test the auxiliary classifier's accuracy on test set

Attributes are now set up with the following sequence in 3 labels problem:

'Black_Hair', 'Blond_Hair', 'Brown_Hair'

Attributes are now set up with the following sequence in 5 labels problem:

'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Male', 'Young'

All the available attributes are:

'5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips',
'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby',
'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male',
'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose',
'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings',
'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young'

which are the attributes provided in celebA dataset. One can change the attribute set in main.py
If the attribute set is changed, the n_label argument should also be changed.

Results

Qualitative comparison between the proposed method (Semi) and a state-of-the-art method StarGAN (Star) over the 12-domain problem. The numbers (1200 to 3600) in the first column are the number of labeled data used. The first row is the target domain.

Others

If one download the data and run the code, 2 folders will be created.

• data/celebA (where the celebA data are placed)
• assets/ (where one's model checkpoint will be saved) If users want to train on their own dataset, please put the dataset under data folders.