  Reaching the performance of fully supervised learning with unlabeled data and
only labeling one sample per class might be ideal for deep learning
applications. We demonstrate for the first time the potential for building
one-shot semi-supervised (BOSS) learning on Cifar-10 and SVHN up to attain test
accuracies that are comparable to fully supervised learning. Our method
combines class prototype refining, class balancing, and self-training. A good
prototype choice is essential and we propose a technique for obtaining iconic
examples. In addition, we demonstrate that class balancing methods
substantially improve accuracy results in semi-supervised learning to levels
that allow self-training to reach the level of fully supervised learning
performance. Rigorous empirical evaluations provide evidence that labeling
large datasets is not necessary for training deep neural networks. We made our
code available at https://github.com/lnsmith54/BOSS to facilitate replication
and for use with future real-world applications.
