| podgorskiy/ALAE |
2,850 |
|
0 |
0 |
about 5 years ago |
0 |
|
31 |
|
Python |
| [CVPR2020] Adversarial Latent Autoencoders |
| YannDubs/disentangling-vae |
668 |
|
0 |
0 |
about 3 years ago |
0 |
|
7 |
other |
Python |
| Experiments for understanding disentanglement in VAE latent representations |
| Schlumberger/joint-vae |
384 |
|
0 |
0 |
about 7 years ago |
0 |
|
1 |
mit |
Jupyter Notebook |
| Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation :star2: |
| znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN |
302 |
|
0 |
0 |
over 7 years ago |
0 |
|
9 |
|
Python |
| Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets |
| znxlwm/pytorch-MNIST-CelebA-cGAN-cDCGAN |
221 |
|
0 |
0 |
over 8 years ago |
0 |
|
6 |
|
Python |
| Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset |
| ermongroup/Variational-Ladder-Autoencoder |
198 |
|
0 |
0 |
over 8 years ago |
0 |
|
1 |
|
Python |
| Implementation of VLAE |
| Yangyangii/GAN-Tutorial |
190 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Simple Implementation of many GAN models with PyTorch. |
| daib13/TwoStageVAE |
160 |
|
0 |
0 |
almost 7 years ago |
0 |
|
7 |
|
Python |
| LynnHo/DCGAN-LSGAN-WGAN-GP-DRAGAN-Pytorch |
145 |
|
0 |
0 |
over 4 years ago |
0 |
|
0 |
mit |
Python |
| DCGAN LSGAN WGAN-GP DRAGAN PyTorch |
| naokishibuya/deep-learning |
128 |
|
0 |
0 |
over 8 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| Deep Learning Application Examples |
