面向在线手写识别的平板与纸张域适应表示学习
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition
AI总结：
针对平板与纸张书写的域偏移导致在线手写识别性能下降问题，提出有监督域适应方法，结合多种损失函数学习域不变特征，通过成对学习与早期融合提升了纸张域识别效果。
摘要：
当机器学习模型被应用于与初始训练数据域相似但不同的数据时，其性能会出现下降。域适应（DA）的目标是通过寻找最优的特征变换以学习域不变表示，从而缓解这种域偏移问题。这种域偏移会出现在手写识别（HWR）应用中——在纸张和平板上书写时，手部运动模式以及随之产生的笔的运动模式存在差异。这种差异在配备集成惯性测量单元的笔采集的在线手写（OnHW）传感器数据中体现得尤为明显。本文提出了一种有监督域适应方法，以增强平板与纸张数据之间在线手写识别的学习效果。我们的方法利用最大均值差异、相关对齐等损失函数来学习域不变特征表示（即平板与纸张特征之间具有相似的协方差）。我们采用三元组损失，引入辅助域的负样本（即纸张样本）来增加平板数据集的样本量。我们在新型基于序列的在线手写数据集（即单词数据集）上进行了评估，结果表明，通过成对学习结合早期融合策略，纸张域的识别性能得到了提升。
英文摘要：
The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of domain adaptation (DA) is to mitigate this domain shift problem by searching for an optimal feature transformation to learn a domain-invariant representation. Such a domain shift can appear in handwriting recognition (HWR) applications where the motion pattern of the hand and with that the motion pattern of the pen is different for writing on paper and on tablet. This becomes visible in the sensor data for online handwriting (OnHW) from pens with integrated inertial measurement units. This paper proposes a supervised DA approach to enhance learning for OnHW recognition between tablet and paper data. Our method exploits loss functions such as maximum mean discrepancy and correlation alignment to learn a domain-invariant feature representation (i.e., similar covariances between tablet and paper features). We use a triplet loss that takes negative samples of the auxiliary domain (i.e., paper samples) to increase the amount of samples of the tablet dataset. We conduct an evaluation on novel sequence-based OnHW datasets (i.e., words) and show an improvement on the paper domain with an early fusion strategy by using pairwise learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
