The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies aim at minimizing the amount of labelled data required to train a DL model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. These techniques are theoretically sound, but an understanding of the selected samples based on their content is not straightforward, further driving non-experts to consider DL as a black-box. For the first time, here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples. In our Knowledge-driven Active Learning (KAL) framework, rule-based knowledge is converted into logic constraints and their violation is checked as a natural guide for sample selection. We show that even simple relationships among data and output classes offer a way to spot predictions for which the model need supervision. We empirically show that KAL (i) outperforms many active learning strategies, particularly in those contexts where domain knowledge is rich, (ii) it discovers data distribution lying far from the initial training data, (iii) it ensures domain experts that the provided knowledge is acquired by the model, (iv) it is suitable for regression and object recognition tasks unlike uncertainty-based strategies, and (v) its computational demand is low.

翻译：深度学习（DL）模型的部署在监督数据有限的场景中仍受到制约。为解决这一问题，主动学习策略旨在最小化训练DL模型所需的标注数据量。大多数主动策略基于不确定性样本选择，甚至通常局限于靠近决策边界的样本。这些技术虽在理论上合理，但基于样本内容理解所选样本并不直观，进一步导致非专业人员将DL视为黑箱。本文首次提出将通用领域知识纳入考量，使非专业用户能够用更少的样本训练模型。在我们的知识驱动主动学习（KAL）框架中，基于规则的知识被转化为逻辑约束，并通过检验这些约束的违反情况作为样本选择的自然引导。研究表明，即使数据与输出类别间的简单关系也能提供一种识别需要模型监督的预测的方式。实验证明，KAL：（i）在领域知识丰富的场景中优于许多主动学习策略；（ii）能发现远离初始训练数据的数据分布；（iii）确保领域专家所提供知识被模型习得；（iv）不同于基于不确定性的策略，适用于回归和物体识别任务；（v）计算需求低。