论文标题
主动传输原型网络：时间序列数据的有效标记算法
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data
论文作者
论文摘要
在汽车行业中，标记数据的匮乏是典型的挑战。注释的时间序列测量需要固体域知识和深入的探索性数据分析，这意味着高标签工作。传统的主动学习（AL）通过根据估计的分类概率进行了最有用的实例来解决此问题，并在迭代中重新审视该模型。但是，学习效率强烈依赖于初始模型，从而导致初始数据集和查询编号的大小之间的权衡。本文提出了一个新颖的几声学习（FSL）基于AL框架，该框架通过将原型网络（Protonet）纳入AL迭代来解决权衡问题。一方面，结果表明了对初始模型的鲁棒性，另一方面，通过在每种迭代中的支持设置的主动选择中的学习效率中的学习效率。该框架已在UCI HAR/HAPT DATASET和现实世界制动动作数据集上进行了验证。学习表现在两个数据集上都显着超过了传统的AL算法，分别以10％和5％的标签工作实现了90％的分类精度。
The paucity of labeled data is a typical challenge in the automotive industry. Annotating time-series measurements requires solid domain knowledge and in-depth exploratory data analysis, which implies a high labeling effort. Conventional Active Learning (AL) addresses this issue by actively querying the most informative instances based on the estimated classification probability and retraining the model iteratively. However, the learning efficiency strongly relies on the initial model, resulting in the trade-off between the size of the initial dataset and the query number. This paper proposes a novel Few-Shot Learning (FSL)-based AL framework, which addresses the trade-off problem by incorporating a Prototypical Network (ProtoNet) in the AL iterations. The results show an improvement, on the one hand, in the robustness to the initial model and, on the other hand, in the learning efficiency of the ProtoNet through the active selection of the support set in each iteration. This framework was validated on UCI HAR/HAPT dataset and a real-world braking maneuver dataset. The learning performance significantly surpasses traditional AL algorithms on both datasets, achieving 90% classification accuracy with 10% and 5% labeling effort, respectively.
