Tensorflow 2.0 port of Gradient Origin Networs paper from the original author's implementation found at - https://github.com/cwkx/GON.
MNIST models (4.3k parameter models) can be trained in under 500 steps to good quality, so they dont have weights restoration. They take just a few minutes to train, even on CPU.
Simply run mnist_example.py, and you it will create a directory called mnist/images which stores 3 kinds of images - reconstruction, sampled images and spherical interpolation between images.
CIFAR 10 models are much larger - 260k parameters, with a larger latent space (256 dim), and need to be trained for a lot longer (100k train steps). Therefore weights have been provided along with log of training.
Simply run cifar10_train.py which takes around 12 - 14 hours on a good GPU to train.
To evaluate results with pre-trained model, run cifar10_eval.py. It will create images under cifar10/images/*_eval.png
pip install -r requirements.txt.
Please cite the original authors for their work -
@article{bondtaylor2020gradient,
title={Gradient Origin Networks},
author={Sam Bond-Taylor and Chris G. Willcocks},
journal={arXiv preprint arXiv:2007.02798},
year={2020}
}