The code is tested on Linux operating system with Python 2.7 or 3.x, TensorFlow 1.4.0+
Paper Address:Training Generative Adversarial Networks Via Turing Test
if you want to run t-sgan_64:
python main.py --gan_type TGAN_64 --loss_type sgan --dataset celebA
t-wgan_64:
python main.py --gan_type TGAN_64 --loss_type wgan --dataset celebA
t-sgan_128:
python main.py --gan_type TGAN_128 --loss_type sgan --dataset celebA
t-wgan_128:
python main.py --gan_type TGAN_128 --loss_type wgan --dataset celebA
you also can try lsgan loss.
Please kindly look at the file main.py
for hyperparameter arguments.
if your tensorflow version >= 1.8, you can set self.custom_dataset = True else self.custom_dataset = False
All results are randomly sampled.
Name | Result-64x64 |
---|---|
T-SGAN | |
T-WGAN |
All results are randomly sampled.
Name | Result-128x128 |
---|---|
T-SGAN | |
T-WGAN |