Semi-Supervised Learning With GANs: Revisiting Manifold Regularization

This is the code we used in our paper

[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

New for IC Design

1. To run the supervised FC classification

   Python3 ICDesign.py #you can play with 2 classes or 3 classes classification

2. To run the semi-supervised GAN classification

   python3 train_ic.py --mode train --epoch 100 --labeled 10 # you can specify the labelled number per class by the parameter --labeled