Recent advances in one-shot semi-supervised learning have lowered the barrier for deep learning of new applications. However, the state-of-the-art for semi-supervised learning is slow to train and the performance is sensitive to the choices of the labeled data and hyper-parameter values. In this paper, we present a one-shot semi-supervised learning method that trains up to an order of magnitude faster and is more robust than state-of-the-art methods. Specifically, we show that by combining semi-supervised learning with a one-stage, single network version of self-training, our FROST methodology trains faster and is more robust to choices for the labeled samples and changes in hyper-parameters. Our experiments demonstrate FROST's capability to perform well when the composition of the unlabeled data is unknown; that is when the unlabeled data contain unequal numbers of each class and can contain out-of-distribution examples that don't belong to any of the training classes. High performance, speed of training, and insensitivity to hyper-parameters make FROST the most practical method for one-shot semi-supervised training.
翻译:以一发半监督式教学的最新进展降低了深入学习新应用程序的障碍。 但是,半监督式学习的先进技术培训速度缓慢,而且性能对标签数据和超参数值的选择十分敏感。 在本文中,我们展示了一种单发半监督式学习方法,这种半监督式学习方法在数量级上更快,而且比最先进的方法更加有力。具体地说,我们通过将半监督式学习与一个阶段、单一的自我培训网络版本相结合,我们的FROST方法培训速度更快,对于标签式样本的选择和超参数的变化来说更加有力。我们的实验表明,FROST在未标数据构成不明时能够很好地运行;这就是,无标签式数据包含每个班级的不平等数字,并且可以包含不属于任何培训班的分流实例。高性、培训速度和对超光度参数的敏感度方法使得FROST的最实用方法成为一发半监督式培训的最实用方法。