- Authors: Steffen Jung and Margret Keuper
- Paper: https://arxiv.org/abs/2012.03110
- Code to train/finetune StyleGAN2: https://github.com/steffen-jung/SpectralGAN-StyleGAN2
Commonly used Generative Adversarial Networks (GANs) are not able to learn the distribution of real datasets in the frequency domain.
Our method adds an additional discriminator increasing the spectral fidelity.
Tested on:
- python 3.8.3
- cudatoolkit 10.1.243
- imageio 2.8.0
- imageio-ffmpeg 0.4.2
- matplotlib 3.2.2
- numpy 1.18.5
- pytorch 1.5.1
- torchvision 0.6.1
- tqdm 4.46.1
Train a new model:
python Training.py \
--device cuda:0 \
--name Debugging \
--experiments_folder /path/to/folder \
--data_folder /path/to/data_folder \
--epochs 100 \
--img_size 64 \
--img_nc 3 \
--loss lsgan \
--d_spectral linear
Continue training:
python Training.py \
--device cuda:0 \
--data_folder /path/to/data_folder \
--epochs 50 \
--img_size 64 \
--img_nc 3 \
--loss lsgan \
--d_spectral linear \
--checkpoint /path/to/previous/runfolder
@inproceedings{Jung2021SpectralGAN,
title = {Spectral Distribution Aware Image Generation},
author = {Steffen Jung and Margret Keuper},
booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence},
year = {2021}
}
- FID score for PyTorch: https://github.com/mseitzer/pytorch-fid