This repository is the official implementation of Sliced Iterative Normalizing Flows.
To install requirements:
pip install -r requirements.txt
To train SIG and GIS, run the following commands:
python SIG.py --dataset DATASET --seed SEED --save SAVING_ADDRESS
python GIS.py --dataset DATASET --seed SEED --save SAVING_ADDRESS
The FID score of SIG samples can also be evaluated on the fly with the "--evaluateFID" argument.
SIG achieves the following performance on :
MNIST | Fashion | CIFAR10 | CelebA |
---|---|---|---|
4.5 | 13.7 | 66.5 | 37.3 |
MNIST | OMNIGLOT | FMNIST-hflip | FMNIST-vflip |
---|---|---|---|
0.990 | 0.993 | 0.631 | 0.821 |