论文标题
构建一击半监督（老板）学习以完全监督的性能
Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
论文作者
论文摘要
通过未标记的数据达到充分监督学习的表现，并且仅标记每个班级的一个样本可能是深度学习应用程序的理想选择。我们首次证明了在CIFAR-10和SVHN上建立一杆半监督（BOSS）学习的潜力，以达到与完全监督学习相当的测试精确度。我们的方法结合了班级原型的精炼，平衡和自我训练。良好的原型选择是必不可少的，我们提出了一种获得标志性示例的技术。此外，我们证明了阶级平衡方法大大提高了准确性，从而在半监督学习中提高了学习水平，从而使自我培训达到完全监督的学习绩效水平。严格的经验评估提供了证据，表明对训练深层神经网络不需要标记大数据集。我们在https://github.com/lnsmith54/boss上提供了代码，以促进复制并与将来的现实世界应用程序一起使用。
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.
