论文标题
LETS-GZSL：时间序列的潜在嵌入模型广义零射击学习
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning
论文作者
论文摘要
深度学习的最新发展之一是广义的零局学习（GZSL），旨在识别所见类和看不见的类别的对象，而仅提供了可见类的标记示例。在过去的几年中，GZSL抓住了牵引力，并提出了几种模型来解决这个问题。尽管在计算机视觉和自然语言处理等领域进行了大量有关GZSL的研究，但尚未进行此类研究来处理时间序列数据。 GZSL用于应用程序，例如检测ECG和EEG数据的异常，并从传感器，光谱仪和其他设备数据中识别出看不见的类。在这方面，我们提出了时间序列的潜在嵌入-GZSL（LETS -GZSL）模型，该模型可以解决时间序列分类（TSC）的GZSL问题。我们利用基于嵌入式的方法，并将其与属性向量相结合以预测最终类标签。我们报告了广泛流行的UCR档案数据集的结果。我们的框架能够在大多数数据集上实现至少55％的谐波平均值，除非看不见的类的数量大于3或数据量非常低（少于100个培训示例）。
One of the recent developments in deep learning is generalized zero-shot learning (GZSL), which aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Over the past couple of years, GZSL has picked up traction and several models have been proposed to solve this problem. Whereas an extensive amount of research on GZSL has been carried out in fields such as computer vision and natural language processing, no such research has been carried out to deal with time series data. GZSL is used for applications such as detecting abnormalities from ECG and EEG data and identifying unseen classes from sensor, spectrograph and other devices' data. In this regard, we propose a Latent Embedding for Time Series - GZSL (LETS-GZSL) model that can solve the problem of GZSL for time series classification (TSC). We utilize an embedding-based approach and combine it with attribute vectors to predict the final class labels. We report our results on the widely popular UCR archive datasets. Our framework is able to achieve a harmonic mean value of at least 55% on most of the datasets except when the number of unseen classes is greater than 3 or the amount of data is very low (less than 100 training examples).
