Sliced Iterative Normalizing Flows
This repository is the official implementation of Sliced Iterative Normalizing Flows.
Requirements
To install requirements:
pip install -r requirements.txt
Training
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
Evaluation
The FID score of SIG samples can also be evaluated on the fly with the "--evaluateFID" argument.
Results
SIG achieves the following performance on :
FID score
| MNIST | Fashion | CIFAR10 | CelebA |
|---|---|---|---|
| 4.5 | 13.7 | 66.5 | 37.3 |
OoD detection (AUROC on models trained on FashionMNIST)
| MNIST | OMNIGLOT | FMNIST-hflip | FMNIST-vflip |
|---|---|---|---|
| 0.990 | 0.993 | 0.631 | 0.821 |