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

Anton Akusok, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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
Number of pages7
Place of PublicationCham
Publication date2021
ISBN (Print)978-3-030-58988-2
ISBN (Electronic)978-3-030-58989-9
Publication statusPublished - 2021
MoE publication typeA4 Article in conference proceedings

Publication series

NameProceedings in Adaptation, Learning and Optimization
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092


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


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