Tensorflow implementation of GANs(Generative Adversarial Networks)
- python 3.5+
- tensorflow 1.1.0+
- scipy
- pillow
- h5py
- pickle
- glob, tqdm
- (sklearn for train_test_split)
(before running train.py, make sure run after downloading dataset & changing dataset directory in train.py)
just download it and run train.py
$ python3 train.py
Now supporting(?) DataSets are... (code is in /datasets.py)
- MNIST
- Cifar-10
- Cifar-100
- Celeb-A
- pix2pix shoes
- pix2pix bags
- (more DataSets will be added soon!)
- ACGAN : Auxiliary Classifier Generative Adversarial Networks [arXiv]
- AdaGAN : Boosting Generative Models [arXiv]
- BEGAN : Boundary Equilibrium Generative Adversarial Networks [arXiv] [code]
- BSGAN : Boundary-Seeking Generative Adversarial Networks [arXiv]
- CGAN : Conditional Generative Adversarial Networks [arXiv] [code]
- CoGAN : Coupled Generative Adversarial Networks [arXiv]
- DCGAN : Deep Convolutional Generative Adversarial Networks [arXiv] [code]
- DiscoGAN : Discover Cross-Domain Generative Adversarial Networks [arXiv] [code]
- EnergyGAN : Energy-based Generative Adversarial Networks [arXiv]
- f-GAN : Training Generative Neural Samplers using Variational Divergence Minimization [arXiv]
- GAN : Generative Adversarial Networks [arXiv] [code]
- Softmax GAN : Generative Adversarial Networks with Softmax [arXiv] [code]
- GAP : Generative Adversarial Parallelization [arXiv]
- InfoGAN : Interpretable Representation Learning by Information Maximizing Generative Adversarial Networks [arXiv]
- LAPGAN : Laplacian Pyramid Generative Adversarial Networks [arXiv]
- LSGAN : Loss-Sensitive Generative Adversarial Networks [arXiv]
- MAGAN : Margin Adaptation for Generative Adversarial Networks [arXiv]
- MRGAN : Mode Regularized Generative Adversarial Networks [arXiv]
- SalGAN : Visual Saliency Prediction Generative Adversarial Networks [arXiv]
- SeqGAN : Sequence Generative Adversarial Networks with Policy Gradient [arXiv]
- SGAN : Stacked Generative Adversarial Networks [arXiv]
- WGAN : Wasserstein Generative Adversarial Networks [arXiv]
- ImprovedWGAN : Improved Training of Wasserstein Generative Adversarial Networks [arXiv]
Hyeongchan Kim / @kozistr, @zer0day