Reinforced Domain Selection for Continuous Domain Adaptation Article Swipe
Hanbing Liu
,
Huaze Tang
,
Yanru Wu
,
Yang Li
,
Xiao–Ping Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/icassp49660.2025.10890287
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/icassp49660.2025.10890287
Continuous Domain Adaptation (CDA) effectively bridges significant domain shifts by progressively adapting from the source domain through intermediate domains to the target domain. However, selecting intermediate domains without explicit metadata remains a substantial challenge that has not been extensively explored in existing studies. To tackle this issue, we propose a novel framework that combines reinforcement learning with feature disentanglement to conduct domain path selection in an unsupervised CDA setting. Our approach introduces an innovative unsupervised reward mechanism that leverages the distances between latent domain embeddings to facilitate the identification of optimal transfer paths. Furthermore, by disentangling features, our method facilitates the calculation of unsupervised rewards using domain-specific features and promotes domain adaptation by aligning domain-invariant features. This integrated strategy is designed to simultaneously optimize transfer paths and target task performance, enhancing the effectiveness of domain adaptation processes. Extensive empirical evaluations on datasets such as Rotated MNIST and ADNI demonstrate substantial improvements in prediction accuracy and domain selection efficiency, establishing our method's superiority over traditional CDA approaches.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icassp49660.2025.10890287
- OA Status
- green
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408354352
All OpenAlex metadata
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https://openalex.org/W4408354352Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icassp49660.2025.10890287Digital Object Identifier
- Title
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Reinforced Domain Selection for Continuous Domain AdaptationWork title
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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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2025-03-12Full publication date if available
- Authors
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Hanbing Liu, Huaze Tang, Yanru Wu, Yang Li, Xiao–Ping ZhangList of authors in order
- Landing page
-
https://doi.org/10.1109/icassp49660.2025.10890287Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2510.10530Direct OA link when available
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Computer science, Domain adaptation, Domain (mathematical analysis), Selection (genetic algorithm), Adaptation (eye), Artificial intelligence, Mathematics, Physics, Classifier (UML), Optics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.optimize | 123 |
| abstract_inverted_index.promotes | 109 |
| abstract_inverted_index.setting. | 68 |
| abstract_inverted_index.strategy | 118 |
| abstract_inverted_index.studies. | 42 |
| abstract_inverted_index.transfer | 91, 124 |
| abstract_inverted_index.Extensive | 137 |
| abstract_inverted_index.challenge | 33 |
| abstract_inverted_index.distances | 80 |
| abstract_inverted_index.empirical | 138 |
| abstract_inverted_index.enhancing | 130 |
| abstract_inverted_index.features, | 96 |
| abstract_inverted_index.features. | 115 |
| abstract_inverted_index.framework | 51 |
| abstract_inverted_index.leverages | 78 |
| abstract_inverted_index.mechanism | 76 |
| abstract_inverted_index.selecting | 24 |
| abstract_inverted_index.selection | 63, 156 |
| abstract_inverted_index.Adaptation | 2 |
| abstract_inverted_index.Continuous | 0 |
| abstract_inverted_index.adaptation | 111, 135 |
| abstract_inverted_index.embeddings | 84 |
| abstract_inverted_index.facilitate | 86 |
| abstract_inverted_index.innovative | 73 |
| abstract_inverted_index.integrated | 117 |
| abstract_inverted_index.introduces | 71 |
| abstract_inverted_index.prediction | 152 |
| abstract_inverted_index.processes. | 136 |
| abstract_inverted_index.approaches. | 165 |
| abstract_inverted_index.calculation | 101 |
| abstract_inverted_index.demonstrate | 148 |
| abstract_inverted_index.effectively | 4 |
| abstract_inverted_index.efficiency, | 157 |
| abstract_inverted_index.evaluations | 139 |
| abstract_inverted_index.extensively | 38 |
| abstract_inverted_index.facilitates | 99 |
| abstract_inverted_index.significant | 6 |
| abstract_inverted_index.substantial | 32, 149 |
| abstract_inverted_index.superiority | 161 |
| abstract_inverted_index.traditional | 163 |
| abstract_inverted_index.Furthermore, | 93 |
| abstract_inverted_index.establishing | 158 |
| abstract_inverted_index.improvements | 150 |
| abstract_inverted_index.intermediate | 17, 25 |
| abstract_inverted_index.performance, | 129 |
| abstract_inverted_index.unsupervised | 66, 74, 103 |
| abstract_inverted_index.disentangling | 95 |
| abstract_inverted_index.effectiveness | 132 |
| abstract_inverted_index.progressively | 10 |
| abstract_inverted_index.reinforcement | 54 |
| abstract_inverted_index.identification | 88 |
| abstract_inverted_index.simultaneously | 122 |
| abstract_inverted_index.disentanglement | 58 |
| abstract_inverted_index.domain-specific | 106 |
| abstract_inverted_index.domain-invariant | 114 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.02881977 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |