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

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...

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Bibliographic Details
Published in:IEEE internet computing 2019-07, Vol.23 (4), p.35-42
Main Authors: Feng, Ziqiang, George, Shilpa, Harkes, Jan, Klatzky, Roberta L., Satyanarayanan, Mahadev, Pillai, Padmanabhan
Format: Article
Language:English
Subjects:
Bandwidth
Cloud computing
Graphical user interfaces
Support vector machines
Training
Training data
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Summary: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.
ISSN:1089-7801
1941-0131
DOI:10.1109/MIC.2019.2892941