Manifold regularization with GANs for semi-supervised learning

This is the code we used in our paper

Manifold regularization with GANs for semi-supervised learning Bruno Lecouat*, Chuan Sheng Foo*, Houssam Zenati, Vijay Ramaseshan Chandrasekhar

Requirements

The repo supports python 3.5 + tensorflow 1.5

Run the Code

To reproduce our results on SVHN

python train_svhn.py

To reproduce our results on CIFAR-10

python train_cifar.py

Results

Here is a comparison of different models using standard architectures on several datasets (SVHN and CIFAR-10):

CIFAR(% errors)	1000 labels	4000 labels
Pi model	5.43 +/- 0.25	16.55 +/- 0.29
Mean Teacher	21.55 +/- 1.48	12.31 +/- 0.28
VAT large		14.18
FM	21.83 +/- 2.01	18.63 +/- 2.32
ALI	19.98 +/- 0.89	17.99 +/- 1.62
Bad GAN		14.41 +/- 0.30
Ours	16.37 +/- 0.42	14.34 +/- 0.17

SVHN (% errors)	500 labels	1000 labels
Pi model	7.05 +/- 0.30	5.43 +/- 0.25
Mean Teacher	4.35 +/- 0.50	3.95 +/- 0.19
VAT small		5.77
FM	18.44 +/- 4.80	8.11 +/- 1.30
ALI		7.41 +/- 0.65
Bad GAN		7.42 +/- 0.65
Ours	5.67 +/- 0.11	4.63 +/- 0.11