IFMix: Utilizing Intermediate Filtered Images for Domain Adaptation in Classification

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Abstract

This paper proposes an iterative intermediate domain generation method using low- and high-pass filters. Domain shift is one of the prime reasons for the poor generalization of trained models in most real-life applications. In a typical case, the target domain differs from the source domain due to either controllable factors (e.g., different sensors) or uncontrollable factors (e.g., weather conditions). Domain adaptation methods bridge this gap by training a domain-invariant network. However, a significant gap between the source and the target domains would still result in bad performance. Gradual domain adaptation methods utilize intermediate domains that gradually shift from the source to the target domain to counter the effect of the significant gap. Still, the assumption of having sufficiently large intermediate domains at hand for any given task is hard to fulfill in real-life scenarios. The proposed method utilizes low- and high-pass filters to create two distinct representatio ns of a single sample. After that, the filtered samples from two domains are mixed with a dynamic ratio to create intermediate domains, which are used to train two separate models in parallel. The final output is obtained by averaging out both models. The method’s effectiveness is demonstrated with extensive experiments on public benchmark datasets: Office-31, Office-Home, and VisDa-2017. The empirical evaluation suggests that the proposed method performs better than the current state-of-the-art works.
Original languageEnglish
Title of host publication Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
PublisherSCITEPRESS
Chapter5
Pages205 - 211
Number of pages7
Volume5
ISBN (Electronic)978-989-758-634-7
DOIs
Publication statusPublished - 26 Mar 2023
Publication typeA4 Article in conference proceedings
EventInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - , Portugal
Duration: 19 Feb 202321 Feb 2023

Publication series

NameVISIGRAPP
PublisherSCITEPRESS - Science and Technology Publications
ISSN (Electronic)2184-4321

Conference

ConferenceInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Abbreviated titleVISIGRAPP
Country/TerritoryPortugal
Period19/02/2321/02/23

Publication forum classification

  • Publication forum level 1

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