This is an implementation of Semi-supervised generative adversarial network in the paper Improved Techniques for Training GANs for Mnist dataset. This method and its extensions have marvellous performance on traditional CV datasets, and remain state-of-art (by the end of November, 2017).

Inspired by Good Semi-supervised Learning that Requires a Bad GAN, semi-supervised GAN with feature matching actually generates unrealistic fake samples around high-density region. With the inborn continuity, the fake region in feature space split the bounds of different classes.

Refer to Semi-supervised Learning on Graphs with Generative Adversarial Nets for more details about this density gap splitting explaination.

The code was implemented in Python 2.7, but I think it also runs well under Python 3.

python ImprovedGAN.py

Default configs include CPU, saving and autoloading, generating logfile in tensorboard format, etc. You can use python ImprovedGAN.py --cuda to run it on GPU.

The latest torch(0.4 version), tensorboardX, torchvision are needed.

Default configs can train models achieving 98.5% accuracy on test dataset with 100 labeled data(10 per class) and other 59,000 unlabeled data after 100 epochs.

loss_label => red, loss_unlabel => blue, loss_gen => green

It must be noted that OpenAI implementation(theano) demonstrates a different curve, where loss_gen is nearly zero and loss_unlabel increase gradually.

• The implementation is based on OpenAI implementation.
• But I found it hard to reproduce expected results and suffered from exploding gradients. I changed the final layer in generator from Sigmoid to Softplus, and therefore fixed it.
• ./models includes the trained model, you can simply delete it for retraining.
• The archectures of networks are elaborately designed, among them Weight Normalization is very important.
• Thank Jiapeng Hong for discussing with me.