Edge-based Privacy-Sensitive Live Learning for Discovery of Training Data

Abstract

Finding true positives (TPs) to construct a training set for a new class of interest in machine learning (ML) is often a challenge. The novelty of the class suggests that cloud archives are unlikely to be helpful. We observe that most video data collected for surveillance and briefly stored at the edge before being overwritten is currently unused. To efficiently harness this untapped resource, we describe Delphi, a privacy-sensitive interactive labeling system that continuously improves labeling productivity through background learning. Our experimental results confirm the value of Delphi for training set construction from edge-sourced data.