In this work we discuss the problem of active learning. We present an approach that is based on A-optimal experimental design of ill-posed problems and show how one can optimally label a data set by partially probing it, and use it to train a deep network. We present two approaches that make different assumptions on the data set. The first is based on a Bayesian interpretation of the semi-supervised learning problem with the graph Laplacian that is used for the prior distribution and the second is based on a frequentist approach, that updates the estimation of the bias term based on the recovery of the labels. We demonstrate that this approach can be highly efficient for estimating labels and training a deep network.

翻译：在这项工作中,我们讨论了积极学习的问题。我们提出了一个基于对问题进行最佳实验设计的方法,并展示了如何通过部分验证对数据集进行最佳标签,并用它来培训深层网络。我们提出了对数据集作出不同假设的两种方法。第一种方法基于巴伊西亚人对半监督学习问题的解读,前者是用于先前分发的拉普拉西亚图,而第二种方法则基于经常使用的方法,以更新基于恢复标签的偏见术语的估算。我们证明这种方法对于估计标签和培训深度网络非常有效。