Eureka: Edge-Based Discovery of Training Data for Machine Learning

被引：7

作者：

Feng, Ziqiang ^{[1
]}

George, Shilpa ^{[1
]}

Harkes, Jan ^{[2
]}

Klatzky, Roberta L. ^{[3
]}

Satyanarayanan, Mahadev ^{[4
]}

Pillai, Padmanabhan ^{[5
]}

机构：

[1] Carnegie Mellon Univ, Dept Comp Sci, Pittsburgh, PA 15213 USA

[2] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

[3] Carnegie Mellon Univ, Psychol & Human Comp Interact, Pittsburgh, PA 15213 USA

[4] Carnegie Mellon Univ, Comp Sci, Pittsburgh, PA 15213 USA

[5] Intel Labs, Santa Clara, CA USA

来源：

IEEE INTERNET COMPUTING | 2019年 / 23卷 / 04期

基金：

美国国家科学基金会;

关键词：

Training; Cloud computing; Training data; Graphical user interfaces; Support vector machines; Bandwidth;

D O I：

10.1109/MIC.2019.2892941

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The generation of high-quality training data has become the key bottleneck in the use of deep learning across many domains. In this paper, we describe Eureka, an interactive system that leverages edge computing and early discard to greatly improve the productivity of experts in the construction of a labeled dataset. 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

页码：35 / 42

页数：8

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