Investigating Feasibility of Active Learning with Image Content on Mobile Devices Using ELM

Anton Akusok, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

This work investigates the feasibility of using the computational resources of a mobile device in active learning usage scenarios. It addresses the two main concerns, namely a way of fast model training or updating when more labels become available without re-training the whole Deep Learning model in case of image analysis, and the feasibility of running active learning workloads directly on a mobile device to improve the responsiveness and avoid using Cloud computing resources that become expensive at a large scale.

The results found that a mobile phone (Apple iPhone Xs in particular) is superior to CPU-bound workloads on a modern laptop. Two special discoveries relate to the latency of the first prection that turns out to be 20x faster on a phone, and some kind of short-lived acceleration after a user touches a phone’s screen that let small batches of up to 20 images to be processed twice faster than usual, in only 0.1 s for a classification of 20 images.
Original languageEnglish
Title of host publicationProceedings of ELM2019
EditorsJiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
PublisherSpringer
Publication date12.09.2020
Pages134-140
ISBN (Print)978-3-030-58988-2
ISBN (Electronic)978-3-030-58989-9
DOIs
Publication statusPublished - 12.09.2020
MoE publication typeA3 Book chapter

Publication series

NameProceedings of ELM2019
Volume14
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092

Keywords

  • 512 Business and Management
  • Extreme Learning Machine
  • iOS
  • Edge computing
  • Active learning
  • 113 Computer and information sciences

Fingerprint Dive into the research topics of 'Investigating Feasibility of Active Learning with Image Content on Mobile Devices Using ELM'. Together they form a unique fingerprint.

Cite this