Enhanced transfer learning with ImageNet trained classification layer

Tasfia Shermin, Shyh Wei Teng, Manzur Murshed, Guojun Lu, Ferdous Sohel, Manoranjan Paul

Research output: Book chapter/Published conference paperConference paperpeer-review

4 Citations (Scopus)

Abstract

Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained source network are transferred to the target network followed by fine-tuning. Prior research has shown that this approach is capable of improving task performance. However, the impact of the ImageNet pre-trained classification layer in parameter fine-tuning is mostly unexplored in the literature. In this paper, we propose a fine-tuning approach with the pre-trained classification layer. We employ layer-wise fine-tuning to determine which layers should be frozen for optimal performance. Our empirical analysis demonstrates that the proposed fine-tuning performs better than traditional fine-tuning. This finding indicates that the pre-trained classification layer holds less category-specific or more global information than believed earlier. Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task. Our study manifests that careful normalization and scaling are essential for creating harmony between the pre-trained and new layers for target domain adaptation. We evaluate the proposed depth augmented networks for fine-tuning on several challenging benchmark datasets and show that they can achieve higher classification accuracy than contemporary transfer learning approaches.

Original languageEnglish
Title of host publicationImage and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings
EditorsChilwoo Lee, Zhixun Su, Akihiro Sugimoto
PublisherSpringer
Pages142-155
Number of pages14
ISBN (Print)9783030348786
DOIs
Publication statusPublished - 01 Jan 2019
Event9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019 - Charles Sturt University Study Centre, Sydney, Australia
Duration: 18 Nov 201922 Nov 2019
http://www.psivt.org/psivt2019/
http://www.psivt.org/psivt2019/program.html (program)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11854 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019
CountryAustralia
CitySydney
Period18/11/1922/11/19
Internet address

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