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
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
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 |
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