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Tensorflow implementation of different GANs and their comparisions

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# 216,365
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Tensorflow implementation of different GANs and their comparisions

GAN implementations

  • [x] DCGAN from 'Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks' (
  • [x] WGAN from 'Wasserstein GAN' (
  • [x] BEGAN from 'BEGAN: Boundary Equilibrium Generative Adversarial Networks' (
  • [x] MAD-GAN from 'Multi-Agent Diverse Generative Adversarial Networks' (
  • [x] GoGAN from 'Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking' (
  • [ ] ... (To be added)


  • [x] Impl. DCGAN, GoGAN, WGAN
  • [x] Impl. BEGAN, MAD-GAN
  • [x] Reproduce GANs on MNIST and CelebA datasets
  • [x] Impl. training & evaluation on synthetic datasets
  • [x] Add sample results
  • [ ] Impl. inference-only code for GANs (may require refactoring)
  • [ ] Impl. better evaluation function for real images (e.g. IvOM, energy dist., ...)
  • [ ] Impl. a result logger
  • [x] Compare GANs (synthetic)
  • [x] Compare GANs (MNIST and CelebA dataset)
  • [ ] Add quantitative comparisons
  • [ ] Add more GAN implementations

Experiments & Benchmarks

170718 / Comparison of different GAN models on synthetic datasets

  • Done without any hyper-parameter search.
  • MAD-GAN worked best in the tested datasets.
  • MADGAN_Spiral

170718 / Sample results on MNIST dataset


170809 / Sample results on CelebA dataset - BEGAN_CELEBA

Other References

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