A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
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README.md

WGAN-GP

An pytorch implementation of Paper "Improved Training of Wasserstein GANs".

Prerequisites

Python, NumPy, SciPy, Matplotlib A recent NVIDIA GPU

A latest master version of Pytorch

Progress

  • gan_toy.py : Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll).(Finished in 2017.5.8)

  • gan_language.py : Character-level language model (Discriminator is using nn.Conv1d. Generator is using nn.Conv1d. Finished in 2017.6.23. Finished in 2017.6.27.)

  • gan_mnist.py : MNIST (Running Results while Finished in 2017.6.26. Discriminator is using nn.Conv1d. Generator is using nn.Conv1d.)

  • gan_64x64.py: 64x64 architectures(Looking forward to your pull request)

  • gan_cifar.py: CIFAR-10(Great thanks to robotcator)

Results

  • Toy Dataset

    Some Sample Result, you can refer to the results/toy/ folder for details.

    • 8gaussians 154500 iteration

    frame1612

    • 25gaussians 48500 iteration

      frame485

    • swissroll 69400 iteration

    frame694

  • Mnist Dataset

    Some Sample Result, you can refer to the results/mnist/ folder for details.

    mnist_samples_91899

    mnist_samples_91899

    mnist_samples_91899

    mnist_samples_199999

  • Billion Word Language Generation (Using CNN, character-level)

    Some Sample Result after 8699 epochs which is detailed in sample

    I haven't run enough epochs due to that this is very time-comsuming.

    He moved the mat all out clame t

    A fosts of shores forreuid he pe

    It whith Crouchy digcloued defor

    Pamreutol the rered in Car inson

    Nor op to the lecs ficomens o fe

    In is a " nored by of the ot can

    The onteon I dees this pirder ,

    It is Brobes aoracy of " medurn

    Rame he reaariod to thim atreast

    The stinl who herth of the not t

    The witl is f ont UAy Y nalence

    It a over , tose sho Leloch Cumm

  • Cifar10 Dataset

    Some Sample Result, you can refer to the results/cifar10/ folder for details.

    mnist_samples_91899

Acknowledge

Based on the implementation igul222/improved_wgan_training and martinarjovsky/WassersteinGAN