IFMix: Utilizing Intermediate Filtered Images for Domain Adaptation in Classification

Saeed Germi, Esa Rahtu

2023

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 representations 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.

Paper Citation

in Harvard Style

Germi S. and Rahtu E. (2023). IFMix: Utilizing Intermediate Filtered Images for Domain Adaptation in Classification. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 205-211. DOI: 10.5220/0011713600003417

in Bibtex Style

@conference{visapp23,
author={Saeed Germi and Esa Rahtu},
title={IFMix: Utilizing Intermediate Filtered Images for Domain Adaptation in Classification},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={205-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011713600003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - IFMix: Utilizing Intermediate Filtered Images for Domain Adaptation in Classification
SN - 978-989-758-634-7
AU - Germi S.
AU - Rahtu E.
PY - 2023
SP - 205
EP - 211
DO - 10.5220/0011713600003417
PB - SciTePress