Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification Article Swipe
Ahmad Chaddad
,
Yihang Wu
,
Yuchen Jiang
,
Ahmed Bouridane
,
Christian Desrosiers
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tim.2025.3527531
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tim.2025.3527531
Traditional machine learning assumes that training and test sets are derived from the same distribution; however, this assumption does not always hold in practical applications. This distribution disparity can lead to severe performance drops when the trained model is used in new data sets. Domain adaptation (DA) is a machine learning technique that aims to address this problem by reducing the differences between domains. This paper presents simulation-based algorithms of recent DA techniques, mainly related to unsupervised domain adaptation (UDA), where labels are available only in the source domain. Our study compares these techniques with public data sets and diverse characteristics, highlighting their respective strengths and drawbacks. For example, Safe Self-Refinement for Transformer-based DA (SSRT) achieved the highest accuracy (91.6\%) in the office-31 data set during our simulations, however, the accuracy dropped to 72.4\% in the Office-Home data set when using limited batch sizes. In addition to improving the reader's comprehension of recent techniques in DA, our study also highlights challenges and upcoming directions for research in this domain. The codes are available at https://github.com/AIPMLab/Domain_Adaptation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tim.2025.3527531
- OA Status
- green
- Cited By
- 4
- References
- 110
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406208317
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4406208317Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tim.2025.3527531Digital Object Identifier
- Title
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Simulations of Common Unsupervised Domain Adaptation Algorithms for Image ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Ahmad Chaddad, Yihang Wu, Yuchen Jiang, Ahmed Bouridane, Christian DesrosiersList of authors in order
- Landing page
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https://doi.org/10.1109/tim.2025.3527531Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2502.10694Direct OA link when available
- Concepts
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Domain adaptation, Computer science, Artificial intelligence, Contextual image classification, Pattern recognition (psychology), Algorithm, Image (mathematics), Statistical classification, Domain (mathematical analysis), Mathematics, Classifier (UML), Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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| publication_year | 2025 |
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| abstract_inverted_index.(SSRT) | 114 |
| abstract_inverted_index.(UDA), | 79 |
| abstract_inverted_index.72.4\% | 133 |
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| abstract_inverted_index.https://github.com/AIPMLab/Domain_Adaptation. | 174 |
| cited_by_percentile_year.max | 98 |
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| countries_distinct_count | 3 |
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| citation_normalized_percentile.is_in_top_1_percent | True |
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