Tensorflow implementation of different GANs and their comparisions
- DCGAN from 'Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks' (https://arxiv.org/abs/1511.06434)
- WGAN from 'Wasserstein GAN' (https://arxiv.org/abs/1701.07875)
- BEGAN from 'BEGAN: Boundary Equilibrium Generative Adversarial Networks' (https://arxiv.org/abs/1703.10717)
- MAD-GAN from 'Multi-Agent Diverse Generative Adversarial Networks' (https://arxiv.org/abs/1704.02906)
- GoGAN from 'Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking' (https://arxiv.org/abs/1704.04865)
- ... (To be added)
- Impl. DCGAN, GoGAN, WGAN
- Impl. BEGAN, MAD-GAN
- Reproduce GANs on MNIST and CelebA datasets
- Impl. training & evaluation on synthetic datasets
- 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
- Compare GANs (synthetic)
- Compare GANs (MNIST and CelebA dataset)
- Add quantitative comparisons
- Add more GAN implementations
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
