Edge-based Discovery of Training Data for Machine Learning

被引:9
|
作者
Feng, Ziqiang [1 ]
George, Shilpa [1 ]
Harkes, Jan [1 ]
Pillai, Padmanabhan [2 ]
Klatzky, Roberta [1 ]
Satyanarayanan, Mahadev [1 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Intel Labs, Pittsburgh, PA USA
基金
美国国家科学基金会;
关键词
D O I
10.1109/SEC.2018.00018
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:145 / 158
页数:14
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