A Unified Deep-Domain Adaptation Framework: Advancing Feature Separability and Local Alignment Article Swipe
Pranav Kumar
,
Jimson Mathew
,
Rakesh Kumar Sanodiya
,
Avinash Chouhan
,
Rahul Reddy Bukkasamudram
,
Chandra Sai Teja Adhikarla
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25123671
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25123671
In transfer learning, domain adaptation is one of the key research areas. For domain adaptation, domain shift is a known problem when the data distribution of the source domain, from which the training data is fetched, and the target domain, from which the test data is fetched, vary significantly. Aligning the source and target domains is a solution, but due to alignment, the intrinsic properties of the data may be altered. To address this issue of domain shift, we introduce a novel method, called “A Unified Deep-Domain Adaptation Framework: Advancing Feature Separability and Local Alignment” (DDASLA) that incorporates an attention mechanism into the ResNet18 model to improve its feature extraction capability. Apart from self-attention, a combined loss function consisting of angular loss, Local Maximum Mean Discrepancy (LMMD), and entropy minimization is used. Angular loss enhances feature discrimination through angular alignment, whereas LMMD equalizes local data distributions across domains, and entropy minimization refines the decision boundaries. A comprehensive experiment on the Office and remote sensing datasets shows that DDASLA outperforms several state-of-the-art methods. These findings show that DDASLA improves model generalization and robustness across domains, paving the way for future domain adaptation research.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25123671
- https://www.mdpi.com/1424-8220/25/12/3671/pdf?version=1749695199
- OA Status
- gold
- Cited By
- 1
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411249469
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- OpenAlex ID
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https://openalex.org/W4411249469Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25123671Digital Object Identifier
- Title
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A Unified Deep-Domain Adaptation Framework: Advancing Feature Separability and Local AlignmentWork 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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2025-06-12Full publication date if available
- Authors
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Pranav Kumar, Jimson Mathew, Rakesh Kumar Sanodiya, Avinash Chouhan, Rahul Reddy Bukkasamudram, Chandra Sai Teja AdhikarlaList of authors in order
- Landing page
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https://doi.org/10.3390/s25123671Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/25/12/3671/pdf?version=1749695199Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/25/12/3671/pdf?version=1749695199Direct OA link when available
- Concepts
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Feature (linguistics), Adaptation (eye), Domain (mathematical analysis), Computer science, Domain adaptation, Artificial intelligence, Pattern recognition (psychology), Mathematics, Psychology, Neuroscience, Mathematical analysis, Philosophy, Classifier (UML), LinguisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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