Edge-Based Discovery of Training Data for Machine Learning
Edge-Based Discovery of Training Data for Machine Learning
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基于边缘的机器学习训练数据发现
DOI：
10.1109/sec.2018.00018
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Satyanarayanan
中科院分区：
文献类型：
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作者：
Ziqiang Feng;S. George;J. Harkes;Padmanabhan Pillai;R. Klatzky;M. Satyanarayanan
我们展示了基于边缘的数据早期丢弃如何极大地提高人类专家在为机器学习组装大型训练集时的生产力。该任务可能跨越多个实时数据源（例如，摄像机）或存档数据源（分散在Internet上的数据集）。这里的关键资源是专家的注意力。我们描述了Eureka，一个利用边缘计算来大大提高专家在这项任务中的生产力的交互式系统。我们的实验结果表明，与暴力方法相比，Eureka将构建训练集所需的标注工作量减少了两个数量级。
We show how edge-based early discard of data can greatly improve the productivity of a human expert in assembling a large training set for machine learning. This task may span multiple data sources that are live (e.g., video cameras) or archival (data sets dispersed over the Internet). The critical resource here is the attention of the expert. We describe Eureka, an interactive system that leverages edge computing to greatly improve the productivity of experts in this task. Our experimental results show that Eureka reduces the labeling effort needed to construct a training set by two orders of magnitude relative to a brute-force approach.