A PyTorch implementation of Temporal Generative Adversarial Nets with Singular Value Clipping.
Although in the original implementation Wasserstein GANs with weight clipping and singular value clipping were used, this version uses the training procedure highlighted in Improved Training of Wasserstein GANs. The model is trained on MovingMNIST.
The configuration options are saved in config.yml. Modify its content accordingly to your needs, for example setting use_cuda to False.
Download the MovingMNIST dataset in the data/ directory using:
wget http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy
To start the training:
python train.py
Savings will be contained in the checkpoints/ directory, while images representing the generated videos are stored in samples/.
I am not one of the authors of the original work nor affiliated with them. To reference their work please use:
@inproceedings{TGAN2017,
author = {Saito, Masaki and Matsumoto, Eiichi and Saito, Shunta},
title = {Temporal Generative Adversarial Nets with Singular Value Clipping},
booktitle = {ICCV},
year = {2017},
}