KAN-CUT

Kolmogorov-Arnold Networks in Contrastive Unpaired Translation (KAN-CUT)

paper

Getting started

• Clone this repo:

git clone https://github.com/amaha7984/KAN-CUT
cd KAN-CUT

KAN-CUT Training and Test

• Download the horse2zebra dataset from CycleGAN github page: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/datasets/download_cyclegan_dataset.sh

The dataset downloaded should be placed in the ./datasets/horse2zebra/.

• Train the KAN-CUT model:

python train.py --dataroot ./datasets/horse2zebra --name horse2zebra_KANCUT --CUT_mode kancut

The checkpoints will be stored at ./checkpoints/ horse2zebra_KANCUT/web.

• Test the trained KAN-CUT model:

python test.py --dataroot ./datasets/ horse2zebra --name horse2zebra_KANCUT --CUT_mode kancut

The test results will be saved to an HTML file located at: ./results/horse2zebra_KANCUT/latest_train/index.html.

KAN-CUT

KAN-CUT is trained with identity preservation loss and with lambda_NCE=1, closely aligning with the CUT approach

[Datasets]

1. The Horse2Zebra dataset can be downloaded from CycleGAN repository. Please visit this link https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
2. The Cat2Dog dataset is a subset of the AFHQ dataset. Please visit https://github.com/clovaai/stargan-v2 and download the AFHQ dataset.

Citation

If you use this code for your research, please cite our paper.

Acknowledgments

Our code is developed based on CUT and efficient-kan. We appreciate the great work provided by CUT. We thank pytorch-fid for facilitating the computation of FID scores.