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