Good Semi-Supervised Learning that Requires a Bad GAN

This is the code we used in our paper

Good Semi-supervised Learning that Requires a Bad GAN

Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Ruslan Salakhutdinov (*: equal contribution)

NIPS 2017

Requirements

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.

Get Pretrained PixelCNN Model

mkdir model
cd model
wget http://kimi.ml.cmu.edu/mnist.True.3.best.pixel

Run the Code

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

Results

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