Reproduce the following GAN-related methods, 100~200 lines each:
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pix2pix (Image-to-image Translation with Conditional Adversarial Networks)
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InfoGAN (InfoGAN: Interpretable Representation Learning by Information Maximizing GAN)
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Conditional GAN
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Improved Wasserstein GAN (Improved Training of Wasserstein GANs)
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DiscoGAN (Learning to Discover Cross-Domain Relations with Generative Adversarial Networks)
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BEGAN (BEGAN: Boundary Equilibrium Generative Adversarial Networks)
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CycleGAN (Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks)
Please see the docstring in each script for detailed usage and pretrained models. MultiGPU training is supported.
Reproduce DCGAN following the setup in dcgan.torch.
- Generated samples
- Vector arithmetic: smiling woman - neutral woman + neutral man = smiling man
Image-to-Image translation following the setup in pix2pix.
For example, with the cityscapes dataset, it learns to generate semantic segmentation map of urban scene:
This is a visualization from tensorboard. Left to right: original, ground truth, model output.
Reproduce the mnist experiement in InfoGAN. It assumes 10 latent variables corresponding to a categorical distribution, 2 latent variables corresponding to a uniform distribution. It then maximizes mutual information between these latent variables and the image, and learns interpretable latent representation.
- Left: 10 latent variables corresponding to 10 digits.
- Middle: 1 continuous latent variable controlled the rotation.
- Right: another continuous latent variable controlled the thickness.
Train a simple GAN on mnist, conditioned on the class labels.
These variants are implemented by some small modifications on top of DCGAN.py. Some BEGAN samples:
Reproduce CycleGAN with the original datasets, and DiscoGAN on CelebA. They are pretty much the same idea with different architecture. CycleGAN horse-to-zebra in tensorboard:
