Fairness across domains: a unified fairness-aware framework for domain generalization and unsupervised adaptation [PDF]
Frontiers in Big DataFairness in machine learning remains a critical challenge, particularly in the presence of domain shift. We propose a unified fairness-aware framework for both domain generalization (DG) and unsupervised domain adaptation (UDA), which jointly addresses ...
Kai Jiang +6 more
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Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics. [PDF]
Genet EpidemiolABSTRACT The Cancer dependency maps (DepMap) identify genetic dependencies in cancer cells using large‐scale loss‐of‐function screens, providing a foundation for cancer‐specific treatment strategies. However, discrepancies exist between cancer cell line models (CCLs) and patient‐derived tumor models, particularly in translating findings to clinical ...
Shi Y, Xu W, Hu P.
europepmc +2 more sources
Unsupervised Domain Adaptation Algorithm for Time Series Based on Adaptive Contrastive Learning [PDF]
EntropyTime series data find extensive applications in finance, healthcare, and industrial monitoring domains. However, analytical models targeting such data are subject to notable constraints imposed by the rigid independent and identically distributed (IID ...
Huayong Liu, Peng Lin
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Unsupervised Domain Adaptation Based on Pseudo-Label Confidence
IEEE Access, 2021 Unsupervised domain adaptation aims to align the distributions of data in source and target domains, as well as assign the labels to data in the target domain.
Tingting Fu, Ying Li
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Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation [PDF]
SensorsPerson re-identification (re-ID) aims to match pedestrian images across disjoint camera views. Existing multi-source unsupervised domain adaptation (UDA) re-ID methods still face two critical issues: they fail to effectively balance domain-invariant ...
Hao Li +4 more
doaj +2 more sources
Histogram matching-enhanced adversarial learning for unsupervised domain adaptation in medical image segmentation. [PDF]
Med PhysAbstract Background Unsupervised domain adaptation (UDA) seeks to mitigate the performance degradation of deep neural networks when applied to new, unlabeled domains by leveraging knowledge from source domains. In medical image segmentation, prevailing UDA techniques often utilize adversarial learning to address domain shifts for cross‐modality ...
Qian X, Shao HC, Li Y, Lu W, Zhang Y.
europepmc +2 more sources
Prototype-oriented class-conditional clustering transport for unsupervised domain adaptation [PDF]
Scientific ReportsUnsupervised domain adaptation (UDA) plays a vital role in machine learning to tackle the homogeneous data distribution scenario. While most previous studies have concentrated on between-domain transferability, they often neglect the rich within-domain ...
Liangda Yan, Jianwen Tao, Tao He
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Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation
Remote Sensing, 2022 Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings.
Ming Liu +3 more
doaj +1 more source
Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation
Frontiers in Neuroscience, 2022 Unsupervised domain adaptation (UDA) is an emerging technique that enables the transfer of domain knowledge learned from a labeled source domain to unlabeled target domains, providing a way of coping with the difficulty of labeling in new domains.
Xiaofeng Liu +8 more
doaj +1 more source
Remote Sensing, 2023 Unsupervised domain adaptation (UDA) is essential since manually labeling pixel-level annotations is consuming and expensive. Since the domain discrepancies have not been well solved, existing UDA approaches yield poor performance compared with ...
Wenjie Liu +3 more
doaj +1 more source