结合源域差异性与目标域不确定性的深度迁移主动学习方法

针对训练深度模型时样本标注成本较大的问题,文中提出结合源域差异性与目标域不确定性的深度迁移主动学习方法.以源任务网络模型作为目标任务初始模型,在主动学习迭代中结合源域差异性和目标域不确定性挑选对模型最具有贡献的目标域样本进行标注,根据学习阶段动态调整两种评价指标的权重.定义信息榨取比概念,提出基于信息榨取比的主动学习批次训练策略及T&N训练策略.两个跨数据集迁移实验表明,文中方法在取得良好性能的同时可有效降低标注成本,提出的主动学习训练策略可优化计算资源在主动学习过程中的分配,即让方法在初始学习阶段对样本学习更多次数,在终末学习阶段对样本学习较少次数.

Deep Transfer Active Learning Method Combining Source Domain Difference and Target Domain Uncertainty

Training deep neural network models comes with a heavy labeling cost. To reduce the cost, a deep transfer active learning method combining source domain and target domain is proposed. With the initial model transferred from source task, the current task samples with larger contribution to the model performance improvement are labeled by using a dynamical weighting combination of source domain difference and target domain uncertainty. Information extraction ratio(IER) is concretely defined in the specific case. An IER-based batch training strategy and a T&N batch training strategy are proposed to deal with model training process. The proposed method is tested on two cross-dataset transfer learning experiments. The results show that the transfer active learning method achieves good performance and reduces the cost of annotation effectively and the proposed strategies optimize the distribution of computing resources during the active learning process. Thus, the model learns more times from samples in the early phases and less times in the later and end phases.