| phillipi/pix2pix |
8,452 |
|
0 |
0 |
almost 5 years ago |
0 |
|
76 |
other |
Lua |
| Image-to-image translation with conditional adversarial nets |
| PaddlePaddle/PaddleGAN |
7,443 |
|
0 |
0 |
over 2 years ago |
6 |
December 08, 2021 |
279 |
apache-2.0 |
Python |
| PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on. |
| carpedm20/DCGAN-tensorflow |
6,761 |
|
0 |
0 |
over 5 years ago |
0 |
|
183 |
mit |
JavaScript |
| A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks" |
| czy36mengfei/tensorflow2_tutorials_chinese |
6,541 |
|
0 |
0 |
over 5 years ago |
0 |
|
15 |
|
Jupyter Notebook |
| tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials |
| udacity/deep-learning-v2-pytorch |
4,973 |
|
0 |
0 |
almost 3 years ago |
0 |
|
15 |
mit |
Jupyter Notebook |
| Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101 |
| udacity/deep-learning |
3,957 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
mit |
Jupyter Notebook |
| Repo for the Deep Learning Nanodegree Foundations program. |
| junyanz/iGAN |
3,672 |
|
0 |
0 |
over 5 years ago |
0 |
|
14 |
mit |
Python |
| Interactive Image Generation via Generative Adversarial Networks |
| yuanxiaosc/DeepNude-an-Image-to-Image-technology |
3,404 |
|
0 |
0 |
almost 6 years ago |
0 |
|
0 |
|
Python |
| DeepNude's algorithm and general image generation theory and practice research, including pix2pix, CycleGAN, UGATIT, DCGAN, SinGAN, ALAE, mGANprior, StarGAN-v2 and VAE models (TensorFlow2 implementation). DeepNude的算法以及通用生成对抗网络（GAN,Generative Adversarial Network）图像生成的理论与实践研究。 |
| yfeng95/GAN |
2,406 |
|
0 |
0 |
over 8 years ago |
0 |
|
|
|
Python |
| Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN |
| robbiebarrat/art-DCGAN |
1,908 |
|
0 |
0 |
about 4 years ago |
0 |
|
19 |
other |
Lua |
| Modified implementation of DCGAN focused on generative art. Includes pre-trained models for landscapes, nude-portraits, and others. |
