sanghoon / Tf Exercise Gan
Licence: mit
Tensorflow implementation of different GANs and their comparisions
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tf-exercise-gan
Tensorflow implementation of different GANs and their comparisions
GAN implementations
- [x] DCGAN from 'Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks' (https://arxiv.org/abs/1511.06434)
- [x] WGAN from 'Wasserstein GAN' (https://arxiv.org/abs/1701.07875)
- [x] BEGAN from 'BEGAN: Boundary Equilibrium Generative Adversarial Networks' (https://arxiv.org/abs/1703.10717)
- [x] MAD-GAN from 'Multi-Agent Diverse Generative Adversarial Networks' (https://arxiv.org/abs/1704.02906)
- [x] GoGAN from 'Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking' (https://arxiv.org/abs/1704.04865)
- [ ] ... (To be added)
Tasks
- [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.
170718 / Sample results on MNIST dataset
170809 / Sample results on CelebA dataset
Other References
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