gan-manifold-reg's Introduction

Manifold regularization with GANs for semi-supervised learning

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

Requirements

The repo supports python 3.5 + tensorflow 1.5

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 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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gan-manifold-reg's Issues

SVHN Baseline Accuracy 95.2%?

Hi, Thanks for your work.

I tested it on the SVHN experiments. But it seems like the model is able to achieve an accuracy of 95.2% only after 200 epoches, without manifold regularization. However, your paper reports 95.37% with manifold regularization after 400 epoches. So I'm wondering if the manifold regularization was not correctly implemented?

Question about output values

Thank you for the code provided.I would like to ask which of the output values is the error rate（14.34） mentioned in the paper？ test acc or test acc ema ？

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