A Collection of modern Deep Generative Models
Project description
DeepGen
A collection of modern Deep Generative Models.
Install
pip3 install deepgen
Usage
Toy example:
import os
import torch
from deepgen.gan.gan import GAN
from torchvision import datasets
import torchvision.transforms as transforms
img_size = (1, 28, 28)
batch_size = 64
if __name__ == '__main__':
data_loader = torch.utils.data.DataLoader(
datasets.MNIST(
'../data/',
train=True,
download=True,
transform=transforms.Compose(
[transforms.Resize(28), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
),
),
batch_size=batch_size,
shuffle=True,
)
os.makedirs('images', exist_ok=True)
model = GAN()
print(model)
his = model.train(data_loader=data_loader, n_epoch=5, sample_interval=10)
print(his)
TODO:
Updating
Generative Adversarial Network (GANs)
- Vanilla GAN
- Boundary Seeking GAN
- Auxiliary Classifier GAN
- BEGAN
- BicycleGAN
- Boundary-Seeking GAN
- Conditional GAN
- Context-Conditional GAN
- Context Encoder
- Coupled GAN
- CycleGAN
- Deep Convolutional GAN
- DiscoGAN
- DRAGAN
- DualGAN
- Energy-Based GAN
- Enhanced Super-Resolution GAN
- GAN
- InfoGAN
- Least Squares GAN
- MUNIT
- Pix2Pix
- PixelDA
- Relativistic GAN
- Semi-Supervised GAN
- Softmax GAN
- StarGAN
- Super-Resolution GAN
- UNIT
- Wasserstein GAN
- Wasserstein GAN GP
- Wasserstein GAN DIV
Variational Autoencoder (VAEs)
- Vanilla VAE
- Conditional VAE
- Denoising VAE
- Adversarial Variational Bayes
- Adversarial Autoencoder
Project details
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