Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Last update: Nov 18, 2022

Overview

Regularizing Generative Adversarial Networks under Limited Data

Implementation for our GAN regularization method. The proposed regularization 1) improves the performance of GANs under limited training data, and 2) complements the exisiting data augmentation approches.

Please note that this is not an officially supported Google product.

Paper

Please cite our paper if you find the code or dataset useful for your research.

Regularizing Generative Adversarial Networks under Limited Data
Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang
Computer Vision and Pattern Recognition (CVPR), 2021

@inproceedings{lecamgan,
  author = {Tseng, Hung-Yu and Jiang, Lu and Liu, Ce and Yang, Ming-Hsuan and Yang, Weilong},
  title = {Regularing Generative Adversarial Networks under Limited Data},
  booktitle = {CVPR},
  year = {2021}
}

Installation and Usage

We provide three implementations: biggan_cifar, biggan_imagenet, and stylegan2. Plesase refer to the README.md file under each sub-folder for the installation and usage guides.

Comments

Training stylegan 2

Hello!

Thanks for the great paper and code!

Unfortunately I am having some issues with integrating the lecam_loss.py with the original stylegan2-ada repo.

I tried hardcoding the functionality of the lecam_loss.py function into the stylegan2 loss in here. It trains but it starts producing aliens and in it quite quickly turns into green images.

Could you please clarify how it is done in more detail?

opened by KasparPeterson 2
Decay factor is 0.99 in the paper but 0.9 in the code

Hello, thanks for sharing this helpful repo! I found that in your code the decay factor is 0.9 which is reported as 0.99 in the paper. Although this is not a critical problem, which value should I exactly use to better reproduce the performance in the paper?

Paper:

We ﬁx the decay factor γ to 0.99 in all experiments.

Code: https://github.com/google/lecam-gan/blob/f9af9485eda4637b25e694c142ce8e6992eb7243/third_party/utils.py#L638

Best.

opened by edward3862 2
ReLU on Discriminator activations?

Thank you for making your implementation available, I'm quite excited to apply the regularizer to ongoing projects.

There was some discussion online recently about a possible mismatch between Eqn 4 in a public preprint of the paper (available here) and the code implementation at these lines: {tf, pytorch}.

Could you please clarify if there's supposed to be a ReLU (present in the code but not the preprint) in Eqn. 4 as well? Thanks again!

opened by neel-dey 2
Implementation for StyleGAN2

Hi I noticed there is only the code for Lecam regularizer in the StyleGAN2 folder. Is the full implementation not available in the repo? Are we supposed to manually add the regularizer to the official StyleGAN2 code ourselves?

opened by mshahbazi72 1
The formula in the paper

The formula (4) in the paper is R_{LC}= E_{x∼T}[||D(x)−\alpha_{F}||^2]+ E_{z∼p_z}[||D(G(z))−\alpha_R||^2] but according to my understanding, this will not make the result converge to a certain point stably, but will cause the result to oscillate, and this does not conform to the legend (2) in the paper, is this a clerical error?

opened by faduo 1
Results in Cifar10

Hi,

It seems like the baseline results in your paper are different from the results in ADA/DA. For example, FID reported for Cifar10 is 5.33 in ADA and 2.68 in your paper. Please advise.

opened by cuikaiwen18 1
Paper link in README is not working

Hi!

The paper link in the readme points to https://github.com/google/lecam-gan/blob/master, which I assume is a mistake. My first thought was that this was maybe by design since CVPR2021 is in June and so the paper isn't public yet, but then I found it here, so I guess this is not the case.

Thanks for your work btw!

opened by ouhenio 1
Unable to run demo file in imagenet folder

The model file is not accessible and call to model file raises the following exception.

model_path=gs://robust-gan/lc-biggan-imagenet128-100/240000 batch_size=2 rm: cannot remove '/tmp/models/*': No such file or directory ServiceException: 401 Anonymous caller does not have storage.objects.list access to the Google Cloud Storage bucket. CommandException: 1 file/object could not be transferred. download model file to :

Can you please provide the file or share where can I get it?

opened by immkapoor 1

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Google

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Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Related tags

Overview

Regularizing Generative Adversarial Networks under Limited Data

Paper

Installation and Usage

Comments

Training stylegan 2

Decay factor is 0.99 in the paper but 0.9 in the code

ReLU on Discriminator activations?

Implementation for StyleGAN2

The formula in the paper

Results in Cifar10

Paper link in README is not working

Unable to run demo file in imagenet folder

Owner

Google

Pytorch implementation for "Adversarial Robustness under Long-Tailed Distribution" (CVPR 2021 Oral)

[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric (ICCV 2021)

The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Official code for the paper: Deep Graph Matching under Quadratic Constraint (CVPR 2021)

This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels].

Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

(under submission) Bayesian Integration of a Generative Prior for Image Restoration

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