Subset Sampling for Progressive Neural Network Learning

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


Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data. While this approach exempts the users from the manual task of designing and validating multiple network topologies, it often requires an enormous number of computations. In this paper, we propose to speed up this process by exploiting subsets of training data at each incremental training step. Three different sampling strategies for selecting the training samples according to different criteria are proposed and evaluated. We also propose to perform online hyperparameter selection during the network progression, which further reduces the overall training time. Experimental results in object, scene and face recognition problems demonstrate that the proposed approach speeds up the optimization procedure considerably while operating on par with the baseline approach exploiting the entire training set throughout the training process.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
Number of pages5
ISBN (Electronic)9781728163956
Publication statusPublished - Oct 2020
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Image Processing - Virtual, Abu Dhabi, United Arab Emirates
Duration: 25 Sep 202028 Sep 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


ConferenceIEEE International Conference on Image Processing
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi


  • Core-set Problem
  • Progressive Neural Network Learning
  • Subset Selection

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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