/GAN-manifold-regularization

This is the code we used in our paper accepted at ICLR 2018

Primary LanguagePython

Semi-Supervised Learning With GANs: Revisiting Manifold Regularization

This is the code we used in our paper accepted at ICLR workshop 2018

[Semi-Supervised Learning With GANs: Revisiting Manifold Regularization]

Bruno Lecouat*, Chuan Sheng Foo*, Houssam Zenati, Vijay Ramaseshan Chandrasekhar

Requirements

The repo supports python 3.5 + tensorflow 1.4

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 (1000 labels on SVHN, and 4000 labels on CIFAR):

Method SVHN (% errors) CIFAR (% errors)
CatGAN - 19.58 +/- 0.46
Ladder Network - 20.40 +/- 0.47
FM 8.11 +/- 1.3 18.63 +/- 2.32
ALI 7.42 +/- 0.65 17.99 +/- 1.62
VAT small 6.83 14.87
Bad GAN 4.25 +/- 0.03 14.41 +/- 0.30
Ours 4.51 +/- 0.22 14.45 +/- 0.21