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sheqi
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Description

A Survey and Taxonomy of the Recent GANs Development & GAN-based Action Recognition Implementations

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News

[2020/11/24] Our paper gets acceptted into ACM Computing Surveys,and we will continue to polish this work into the 5th version.

[2020/06/20] We have updated our 4th version of GAN survey paper ! It inlcudes more recent GANs proposed at CVPR, ICCV 2019/2020, more intuitive visualization of GAN Taxonomy.

[2020/10/04] GANs related to our latest paper will be updated shortly.

GAN_Review

A Survey and Taxonomy of the Recent GANs Development. Please refer to the details in recent review paper “Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy” Zhengwei Wang and Qi She and Tomas E. Ward. We also provide a list of papers related to GANs on computer vision in the GAN_CV.csv file.

If you find this useful in your research, please consider citing:

@article{wang2021generative,
  title={Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy},
  author={Wang, Zhengwei and She, Qi and Ward, Tomas E},
  journal={ACM Computing Surveys (CSUR)},
  volume={54},
  number={2},
  pages={1--38},
  year={2021},
  publisher={ACM New York, NY, USA}
}

We have classified the two GAN-variants research lines based on recent GAN developments, below we provide a summary and the demo code of these models. We have tested the codes below and tried to summary some of lightweight and easy-to-reuse module of state-of-the-art GANs.

Architecture-variant GANs

LAPGAN:
https://github.com/jimfleming/LAPGAN (TensorFlow)
https://github.com/AaronYALai/GenerativeAdversarialNetworks_PyTorch (PyTorch)

DCGAN:
https://github.com/carpedm20/DCGAN-tensorflow (TensorFlow)
https://github.com/last-one/DCGAN-Pytorch (PyTorch)

BEGAN:
https://github.com/carpedm20/BEGAN-tensorflow (TensorFlow)
https://github.com/anantzoid/BEGAN-pytorch (PyTorch)

PROGAN:
https://github.com/tkarras/progressivegrowingof_gans (TensorFlow)
https://github.com/nashory/pggan-pytorch (PyTorch)

SAGAN:
https://github.com/brain-research/self-attention-gan (TensorFlow)
https://github.com/heykeetae/Self-Attention-GAN (PyTorch)

BigGAN:
https://github.com/taki0112/BigGAN-Tensorflow (TensorFlow)
https://github.com/ajbrock/BigGAN-PyTorch (PyTorch)

Your Local GAN:
https://github.com/giannisdaras/ylg (TensorFlow)
https://github.com/188zzoon/Your-Local-GAN (PyTorch)

AutoGAN:
https://github.com/VITA-Group/AutoGAN (PyTorch)

MSG-GAN:
https://github.com/akanimax/msg-stylegan-tf (TensorFlow)
https://github.com/akanimax/msg-gan-v1 (PyTorch)

Loss-variant GANs

WGAN:
https://github.com/ChengBinJin/WGAN-TensorFlow (TensorFlow)
https://github.com/Zeleni9/pytorch-wgan (PyTorch)

WGAN-GP:
https://github.com/changwoolee/WGAN-GP-tensorflow (TensorFlow)
https://github.com/caogang/wgan-gp (PyTorch)

LSGAN:
https://github.com/xudonmao/LSGAN (TensorFlow)
https://github.com/meliketoy/LSGAN.pytorch (PyTorch)

f-GAN:
https://github.com/LynnHo/f-GAN-Tensorflow (TensorFlow)

UGAN:
https://github.com/gokul-uf/TF-Unrolled-GAN (TensorFlow)
https://github.com/andrewliao11/unrolled-gans (PyTorch)

LS-GAN:
https://github.com/maple-research-lab/lsgan-gp-alt (TensorFlow)
https://github.com/maple-research-lab/glsgan-gp (PyTorch)

MRGAN:
https://github.com/wiseodd/generative-models/tree/master/GAN/moderegularizedgan (TensorFlow and PyTorch)

Geometric GAN:
https://github.com/lim0606/pytorch-geometric-gan (PyTorch)

RGAN:
https://github.com/AlexiaJM/RelativisticGAN (TensorFlow and PyTorch)

SN-GAN:
https://github.com/taki0112/Spectral_Normalization-Tensorflow (TensorFlow) https://github.com/christiancosgrove/pytorch-spectral-normalization-gan (PyTorch)

RealnessGAN:
https://github.com/taki0112/RealnessGAN-Tensorflow (TensorFlow)
https://github.com/kam1107/RealnessGAN (PyTorch)

Sphere GAN:
https://github.com/taki0112/SphereGAN-Tensorflow (TensorFlow)
https://github.com/Dotori-HJ/SphereGAN-Pytorch-implementation (PyTorch)

Self-supervised GAN:
https://github.com/zhangqianhui/Self-Supervised-GANs (TensorFlow)
https://github.com/vandit15/Self-Supervised-Gans-Pytorch (PyTorch)

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