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README.md
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README.md
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@ -5,7 +5,7 @@ Code based on [Pytorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN)
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Our GAN model zoo supports 31 kinds of GAN.
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This table is the latest citations we found from Google Scholar.
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It can be seen that since GAN was proposed in 2014, a lot of excellent work based on GAN has appeared.
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These 28 GANs have a total of 60953 citations, with an average of 2176 citations per article.
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These 27 GANs have a total of 60953 citations, with an average of 2176 citations per article.
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<p align="center">
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<img src="assets/cite.png"\>
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@ -39,9 +39,7 @@ In another form of presentation, assuming that Pytorch's training time is 100 ho
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+ [Coupled GAN](#coupled-gan)
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+ [CycleGAN](#cyclegan)
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+ [Deep Convolutional GAN](#deep-convolutional-gan)
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+ [DiscoGAN](#discogan)
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+ [DRAGAN](#dragan)
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+ [DualGAN](#dualgan)
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+ [Energy-Based GAN](#energy-based-gan)
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+ [Enhanced Super-Resolution GAN](#enhanced-super-resolution-gan)
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+ [GAN](#gan)
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@ -53,7 +51,6 @@ In another form of presentation, assuming that Pytorch's training time is 100 ho
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+ [Semi-Supervised GAN](#semi-supervised-gan)
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+ [Softmax GAN](#softmax-gan)
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+ [StarGAN](#stargan)
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+ [Super-Resolution GAN](#super-resolution-gan)
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+ [UNIT](#unit)
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+ [Wasserstein GAN](#wasserstein-gan)
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+ [Wasserstein GAN GP](#wasserstein-gan-gp)
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@ -288,31 +285,6 @@ $ python3.7 dcgan.py
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<img src="assets/dcgan.png" width="240"\>
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</p>
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### DiscoGAN
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_Learning to Discover Cross-Domain Relations with Generative Adversarial Networks_
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#### Authors
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Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim
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[[Paper]](https://arxiv.org/abs/1703.05192) [[Code]](models/discogan/discogan.py)
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#### Run Example
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```
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$ cd data/
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$ bash download_pix2pix_dataset.sh edges2shoes
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$ cd ../models/discogan/
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$ python3.7 discogan.py --dataset_name edges2shoes
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```
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<p align="center">
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<img src="assets/discogan.gif" width="200"\>
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</p>
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<p align="center">
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Rows from top to bottom: (1) Real image from domain A (2) Translated image from <br>
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domain A (3) Reconstructed image from domain A (4) Real image from domain B (5) <br>
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Translated image from domain B (6) Reconstructed image from domain B
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</p>
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### DRAGAN
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_On Convergence and Stability of GANs_
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@ -335,28 +307,6 @@ $ python3.7 dragan.py
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</p>
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### DualGAN
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_DualGAN: Unsupervised Dual Learning for Image-to-Image Translation_
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#### Authors
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Zili Yi, Hao Zhang, Ping Tan, Minglun Gong
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[[Paper]](https://arxiv.org/abs/1704.02510) [[Code]](models/dualgan/dualgan.py)
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#### Run Example
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```
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$ cd data/
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$ bash download_pix2pix_dataset.sh facades
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$ cd ../models/dualgan/
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$ python3.7 dualgan.py --dataset_name facades
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```
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<p align="center">
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<img src="assets/dualgan.gif" width="240"\>
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</p>
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### Energy-Based GAN
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_Energy-based Generative Adversarial Network_
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@ -583,21 +533,6 @@ $ python3.7 stargan.py
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Original | Black Hair | Blonde Hair | Brown Hair | Gender Flip | Aged
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</p>
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### Super-Resolution GAN
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_Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network_
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#### Authors
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Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
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[[Paper]](https://arxiv.org/abs/1609.02002) [[Code]](models/srgan/srgan.py)
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#### Run Example
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```
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$ cd models/srgan/
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<follow steps at the top of srgan.py>
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$ python3.7 srgan.py
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```
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### UNIT
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_Unsupervised Image-to-Image Translation Networks_
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