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 3.7.
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(1.2 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.
./modelsincludes the trained model, you can simply delete it for retraining.- The archectures of networks are elaborately designed, among them
Weight Normalizationis very important. - Thank Jiapeng Hong for discussing with me.
- (Nov 27, 2019) Update to pytorch 1.2 and Python 3.7. The version for pytorch 0.3 and Python 2.7 can be found in the history versions. Delete pretrained models.