This is a simple Tensorflow implementation of the Semi-Supervised GAN proposed in the paper Improved Techniques for Training GANs from Salimans et al. https://arxiv.org/abs/1606.03498
The code reproduces the results presented in the original paper. It uses the same tricks than the original Theano implementation.
The repo supports python 3.5 + tensorflow 1.5
To reproduce our results on SVHN
python train_svhn.py
To reproduce our results on CIFAR-10
python train_cifar.py
| CIFAR(% errors) | 1000 labels | 4000 labels |
|---|---|---|
| Improved GAN (ours) | 20.24 +/- 2.17 | 17.34 +/- 1.97 |
| SVHN(% errors) | 400 labels | 1000 labels |
|---|---|---|
| Improved GAN (ours) | 5.22 +/- 1.02 | 4.12 +/- 1.23 |