论文标题
Clrgaze：对比度学习眼动信号的表示形式
CLRGaze: Contrastive Learning of Representations for Eye Movement Signals
论文作者
论文摘要
眼睛运动是复杂的，动态的生物信号，其中包含有关该主题的大量认知信息。但是，这些是模棱两可的信号，因此需要机器学习算法使用细致的功能工程。相反，我们建议以一种自我监督的方式学习眼睛运动的特征向量。我们采用一种对比的学习方法，并提出了一组数据转换，这些数据转换鼓励深层神经网络辨别显着和颗粒状的凝视模式。本文提出了一项新的实验，该实验利用六个引人注目的数据集，尽管数据规格不同和实验条件。我们仅使用线性分类器来评估生物识别任务上的学习功能，在混合数据集上达到了84.6％的精度，并且在单个数据集上的精度最高为97.3％。我们的工作推进了眼动的机器学习状态，并为一种通用表示方法提供了洞察力，不仅是眼动动作，而且还针对类似的生物信号。
Eye movements are intricate and dynamic biosignals that contain a wealth of cognitive information about the subject. However, these are ambiguous signals and therefore require meticulous feature engineering to be used by machine learning algorithms. We instead propose to learn feature vectors of eye movements in a self-supervised manner. We adopt a contrastive learning approach and propose a set of data transformations that encourage a deep neural network to discern salient and granular gaze patterns. This paper presents a novel experiment utilizing six eye-tracking data sets despite different data specifications and experimental conditions. We assess the learned features on biometric tasks with only a linear classifier, achieving 84.6% accuracy on a mixed dataset, and up to 97.3% accuracy on a single dataset. Our work advances the state of machine learning for eye movements and provides insights into a general representation learning method not only for eye movements but also for similar biosignals.
