This is the code we used in our paper
Zihang Dai*, Zhilin Yang*, Fan Yang, William W. Cohen, Ruslan Salakhutdinov (*: equal contribution)
NIPS 2017
The repo supports python 2.7 pytorch 0.1.12. To install pytorch 0.1.12, run conda install pytorch=0.1.12 cuda80 -c soumith.
mkdir model
cd model
wget http://kimi.ml.cmu.edu/mnist.True.3.best.pixel
To reproduce our results on MNIST
python mnist_trainer.py
To reproduce our results on SVHN
python svhn_trainer.py
To reproduce our results on CIFAR-10
python cifar_trainer.py
Here is a comparison of different models using standard architectures without ensembles (100 labels on MNIST, 1000 labels on SVHN, and 4000 labels on CIFAR):
| Method | MNIST (# errors) | SVHN (% errors) | CIFAR (% errors) |
|---|---|---|---|
| CatGAN | 191 /- 10 | - | 19.58 /- 0.46 |
| SDGM | 132 /- 7 | 16.61 /- 0.24 | - |
| Ladder Network | 106 /- 37 | - | 20.40 /- 0.47 |
| ADGM | 96 /- 2 | 22.86 | - |
| FM | 93 /- 6.5 | 8.11 /- 1.3 | 18.63 /- 2.32 |
| ALI | - | 7.42 /- 0.65 | 17.99 /- 1.62 |
| VAT small | 136 | 6.83 | 14.87 |
| Ours | 79.5 /- 9.8 | 4.25 /- 0.03 | 14.41 /- 0.30 |