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The Fusion of Neural Architecture Search and Destruction and Construction Learning: First Classified

  • Beihang University
  • Shenzhen Academy of Aerospace Technology

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

Abstract

Object classification is a classic problem in the field of pattern recognition. The traditional deep neural networks have been able to achieve good results on some classification problems, however, there are still many difficulties to be overcome in the fine-grained identification task, whose performance are still baffled by practical problems. In this paper, we introduce neural architecture search (NAS) to search the appropriate network according to the specific data set, which do not need more engineering work to adjust parameters for the optimized performance. We further combine the Destruction and Construction Learning (DCL) network and the NAS-based network for pollen recognition. To this end, we use a fusion algorithm to implement the combination of different networks, and won the pollen recognition competition held at the international pattern recognition Conference (ICPR) 2020.

Original languageEnglish
Title of host publicationPattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
EditorsAlberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
PublisherSpringer Science and Business Media Deutschland GmbH
Pages480-489
Number of pages10
ISBN (Print)9783030687922
DOIs
StatePublished - 2021
Event25th International Conference on Pattern Recognition Workshops, ICPR 2021 - Virtual, Online, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameLecture Notes in Computer Science
Volume12668 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Pattern Recognition Workshops, ICPR 2021
Country/TerritoryItaly
CityVirtual, Online
Period01/10/2101/15/21

Keywords

  • Destruction and Construction Learning
  • Model fusion
  • Neural architecture search

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