In recent years, neural networks achieved much success in various applications. The main challenge in training deep neural networks is the lack of sufficient data to improve the model's generalization and avoid overfitting. One of the solutions is to generate new training samples. This paper proposes a novel data augmentation method for time series based on Dynamic Time Warping. This method is inspired by the concept that warped parts of two time series have similar temporal properties and therefore, exchanging them between the two series generates a new training sample. The proposed method selects an element of the optimal warping path randomly and then exchanges the segments that are aligned together. Exploiting the proposed approach with recently introduced ResNet reveals improved results on the 2018 UCR Time Series Classification Archive. By employing Gradient-weighted Class Activation Mapping (Grad-CAM) and Multidimensional Scaling (MDS), we manifest that our method extract more discriminant features out of time series.
Deep Learning Based Walking Tasks Classification in Older Adults using fNIRS
Villanova University（维拉诺瓦大学）；Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院）；Ferkauf Graduate School of Psychology of Yeshiva University（叶史瓦大学费尔考夫心理学研究生院）
PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series
Department of Computer Science（计算机科学系）；Indiana University（印第安纳大学）；Department of Biomedical Engineering（生物医学工程系）；Oregon Health & Science University（俄勒冈健康与科学大学）
Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach
Department of Computer Science, University of Kaiserslautern-Landau (RPTU)（卡斯尔拉特-兰道大学计算机科学系）；SDS, German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
