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 practical 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 \url{https://github.com/lnsmith54/BOSS} to facilitate replication and for use with future real-world applications.

本文提出了一种基于半监督学习、一次性学习和类平衡的方法，通过精心选择类原型并改进模型结构，使得无需大量标注数据进行训练的深度神经网络可以达到与全监督学习相当的测试准确性。同时，本文通过严谨的实证评估证明了标注大规模数据不是训练深度神经网络的必要条件。

构建一次半监督学习（BOSS）可提高完全监督的性能