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

Anton Akusok, Amaury Lendasse

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
OriginalspråkEngelska
Titel på gästpublikationProceedings of ELM2019
RedaktörerJiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Antal sidor7
UtgivningsortCham
FörlagSpringer
Utgivningsdatum2021
Sidor134-140
ISBN (tryckt)978-3-030-58988-2
ISBN (elektroniskt)978-3-030-58989-9
DOI
StatusPublicerad - 2021
MoE-publikationstypA4 Artikel i en konferenspublikation

Publikationsserier

NamnProceedings in Adaptation, Learning and Optimization
Volym14
ISSN (tryckt)2363-6084
ISSN (elektroniskt)2363-6092

Nyckelord

  • 512 Företagsekonomi
  • 113 Data- och informationsvetenskap

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