Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Mit Deep Learning | 9,328 | 5 months ago | 15 | mit | Jupyter Notebook | |||||
Tutorials, assignments, and competitions for MIT Deep Learning related courses. | ||||||||||
Deeplearning | 7,463 | a year ago | 8 | apache-2.0 | Jupyter Notebook | |||||
深度学习入门教程, 优秀文章, Deep Learning Tutorial | ||||||||||
Tensorlayer | 7,161 | 34 | 6 | a month ago | 83 | February 15, 2022 | 30 | other | Python | |
Deep Learning and Reinforcement Learning Library for Scientists and Engineers | ||||||||||
Dcgan Tensorflow | 6,761 | 2 years ago | 183 | mit | JavaScript | |||||
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks" | ||||||||||
Generative Models | 6,010 | 4 years ago | 18 | unlicense | Python | |||||
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. | ||||||||||
T81_558_deep_learning | 5,225 | 16 days ago | 2 | other | Jupyter Notebook | |||||
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks | ||||||||||
Tensorflow Tutorial | 3,873 | 2 years ago | 7 | mit | Python | |||||
Tensorflow tutorial from basic to hard | ||||||||||
Animegan | 3,738 | 8 months ago | 17 | Python | ||||||
A Tensorflow implementation of AnimeGAN for fast photo animation ! This is the Open source of the paper 「AnimeGAN: a novel lightweight GAN for photo animation」, which uses the GAN framwork to transform real-world photos into anime images. | ||||||||||
Tensorflow Generative Model Collections | 3,570 | 5 years ago | 22 | apache-2.0 | Python | |||||
Collection of generative models in Tensorflow | ||||||||||
Image Super Resolution | 3,376 | a year ago | 85 | apache-2.0 | Python | |||||
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks. |
Creats new kinds of pokemons using WGAN! (DCGAN is also supported)
cv2
tensorflow( >=1.0)
scipy
numpy
git clone ‘https://github.com/moxiegushi/pokeGAN.git’
cd pokeGAN
python resize.py
python RGBA2RGB.py
python pokeGAN.py
It is difficult to train a GAN perfectly, and as you can see someimages are meaningless. I'm very new to this. Please let me know if there are bugs :)