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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

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