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Human-efficient Discovery of Edge-based Training Data for Visual Machine Learning

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thesis
posted on 04.05.2022, 18:56 by Ziqiang FengZiqiang Feng

Deep learning enables effective computer vision without hand crafting feature extractors. It has great potential if applied to specialized domains such as ecology, military, and medical science. However, the laborious task of creating labeled training sets of rare targets is a major deterrent to achieving its goal. A domain expert’s time and attention is precious. We address this problem by designing, implementing, and evaluating Eureka, a system for human-efficient discovery of rare phenomena from unlabeled visual data. Eureka’s central idea is interactive contentbased

search of visual data based on early-discard and machine learning. We first demonstrate its effectiveness for curating training sets of rare objects. By analyzing contributing factors to human efficiency, we identify and evaluate important systemlevel optimizations that utilize edge computing and intelligent storage. Lastly, we extend Eureka to the task of discovering temporal events from video data.

History

Date

23/08/2021

Degree Type

Dissertation

Department

Computer Science

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Mahadev Satyanarayanan

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