  Compared to supervised learning, semi-supervised learning reduces the
dependence of deep learning on a large number of labeled samples. In this work,
we use a small number of labeled samples and perform data augmentation on
unlabeled samples to achieve image classification. Our method constrains all
samples to the predefined evenly-distributed class centroids (PEDCC) by the
corresponding loss function. Specifically, the PEDCC-Loss for labeled samples,
and the maximum mean discrepancy loss for unlabeled samples are used to make
the feature distribution closer to the distribution of PEDCC. Our method
ensures that the inter-class distance is large and the intra-class distance is
small enough to make the classification boundaries between different classes
clearer. Meanwhile, for unlabeled samples, we also use KL divergence to
constrain the consistency of the network predictions between unlabeled and
augmented samples. Our semi-supervised learning method achieves the
state-of-the-art results, with 4000 labeled samples on CIFAR10 and 1000 labeled
samples on SVHN, and the accuracy is 95.10% and 97.58% respectively.
