Results 71 to 80 of about 4,969,023 (298)
Bilateral co-transfer for unsupervised domain adaptation
Journal of Automation and Intelligence, 2023 Labeled data scarcity of an interested domain is often a serious problem in machine learning. Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested domain is a consensus.
Fuxiang Huang, Jingru Fu, Lei Zhang
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Domain Adaptation for Statistical Classifiers
, 2011 The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related,
Daume III, H., Marcu, D.
core +1 more source
Discriminativeness-Preserved Domain Adaptation for Few-Shot Learning
IEEE Access, 2020 Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples.
Guangzhen Liu, Zhiwu Lu
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Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
, 2017 Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.
Chung, Fu-lai +5 more
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Meta Domain Adaptation Approach for Multi-Domain Ranking
IEEE AccessIn a real industry recommendation system, the distribution of recommended domains is very redundant. Different domains may address the same problem, such as the Click-Through Rate (CTR) prediction, and may share the same features.
Zihan Xia +4 more
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Contrastive Adaptation Network for Unsupervised Domain Adaptation [PDF]
Computer Vision and Pattern Recognition, 2019 Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to ...
Guoliang Kang +3 more
semanticscholar +1 more source
Structure preserved ordinal unsupervised domain adaptation
Electronic Research ArchiveUnsupervised domain adaptation (UDA) aims to transfer the knowledge from labeled source domain to unlabeled target domain. The main challenge of UDA stems from the domain shift between the source and target domains.
Qing Tian, Canyu Sun
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Self-Supervised Domain Adaptation for Computer Vision Tasks
IEEE Access, 2019 Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored.
Jiaolong Xu +2 more
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Sensors, 2021 In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can
Xudong Li +4 more
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Psychosocial Outcomes in Patients With Endocrine Tumor Syndromes: A Systematic Review
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction The combination of disease manifestations, the familial burden, and varying penetrance of endocrine tumor syndromes (ETSs) is unique. This review aimed to portray and summarize available data on psychosocial outcomes in patients with ETSs and explore gaps and opportunities for future research and care.
Daniël Zwerus +6 more
wiley +1 more source