Semi-Supervised Learning Based on GAN With Mean and Variance Feature Matching

The improved generative adversarial network (improved GAN) is a successful method using a generative adversarial model to solve the problem of semi-supervised learning (SSL). The improved GAN learns a generator with the technique of mean feature matching which penalizes the discrepancy of the first-order moment of the latent features. To better describe common attributes of a distribution, this paper proposes a novel SSL method which incorporates the first-order and the second-order moments of the features in an intermediate layer of the discriminator, called mean and variance feature matching GAN (MVFM-GAN). To capture more precisely the data manifold, not only the mean but also the variance is used in the latent feature learning. Compared with improved GAN and other traditional methods, MVFM-GAN achieves superior performance in semi-supervised classification tasks and a better stability of GAN training, particularly in the cases when the number of labeled samples is low. It shows a comparable performance with the state-of-the-art methods on several benchmark data sets. As a byproduct of the novel approach, MVFM-GAN generates realistic images of good visual quality.