The success of deep active learning hinges on the choice of an effective acquisition function, which ranks not yet labeled data points according to their expected informativeness. Many acquisition functions are (partly) based on the uncertainty that the current model has about the class label of a point, yet there is no generally agreed upon strategy for computing such uncertainty. This paper proposes a new and very simple approach to computing uncertainty in deep active learning with a Convolutional Neural Network (CNN). The main idea is to use the feature representation extracted by the CNN as data for training a Sum-Product Network (SPN). Since SPNs are typically used for estimating the distribution of a dataset, they are well suited to the task of estimating class probabilities that can be used directly by standard acquisition functions such as max entropy and variational ratio. The effectiveness of our method is demonstrated in an experimental study on several standard benchmark datasets for image classification, where we compare it to various state-of-the-art methods for assessing uncertainty in deep active learning.

翻译：深度主动学习的成功取决于有效采集函数的选择，该函数根据未标注数据点的预期信息量对其进行排序。许多采集函数（部分）基于当前模型对数据点类别标签的不确定性，但目前尚无普遍认可的计算此类不确定性的策略。本文提出一种全新且极为简单的方法，用于在基于卷积神经网络（CNN）的深度主动学习中计算不确定性。核心思想是将CNN提取的特征表示作为训练和积网络（SPN）的数据。由于SPN通常用于估计数据集的分布，它们非常适合于估计可直接用于标准采集函数（如最大熵和变分比）的类别概率。通过在多个标准图像分类基准数据集上的实验研究，我们验证了该方法的有效性，并与多种评估深度主动学习中不确定性的前沿方法进行了比较。