Ziqiang Feng

Human-efficient Discovery of Edge-based Training Data for Visual Machine Learning Degree Type: Ph.D. in Computer Science
Advisor(s): Mahadev Satyanarayanan
Graduated: August 2021

Abstract:

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 content-based 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 system-level optimizations that utilize edge computing and intelligent storage. Lastly, we extend Eureka to the task of discovering temporal events from video data.

Thesis Committee:
Mahadev Satyanarayanan (Chair)
Martial Hebert
Roberta Klatzky
Padmanabhan Pillai (Intel Labs)

Srinivasan Seshan, Head, Computer Science Department
Martial Hebert, Dean, School of Computer Science

Keywords:
Edge Computing, Cloudlet, Video Analytics, Training Data