Chongxuan Li, Kun Xu, Jun Zhu and Bo Zhang
Code for reproducing most of the results in the paper. Triple-GAN: a unified GAN model for classification and class-conditional generation in semi-supervised learning.
Warning: the code is still under development.
Python Numpy Scipy Theano Lasagne(version 0.2.dev1) Parmesan
Thank the authors of these libs. We also thank the authors of Improved-GAN and Temporal Ensemble for providing their code. Our code is widely adapted from their repositories.
Triple-GAN can achieve excellent classification results on MNIST, SVHN and CIFAR10 datasets, see the paper for a comparison with previous state-of-the-art. See generated images as follows:
Comparing Triple-GAN (right) with GAN trained with feature matching (left)
