Results 51 to 60 of about 6,614,138 (295)
Multi-source domain adaptation for regression
CoRR, 2023 Multi-source domain adaptation (DA) aims at leveraging information from more than one source domain to make predictions in a target domain, where different domains may have different data distributions. Most existing methods for multi-source DA focus on classification problems while there is only limited investigation in the regression settings.
Yujie Wu, Giovanni Parmigiani, Boyu Ren
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, 2023 The prevalence of domain adaptive semantic segmentation has prompted concerns regarding source domain data leakage, where private information from the source domain could inadvertently be exposed in the target domain.
Chen, Zhi +4 more
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Domain Aggregation Networks for Multi-Source Domain Adaptation
CoRR, 2019 In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing characters of a new font using a set of different fonts. While most recent research has considered ad-hoc combination rules to address this problem, we extend previous work on ...
Junfeng Wen +2 more
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Active Source Free Domain Adaptation
CoRR, 2022 Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an effect bottleneck due to the absence of source data and target supervised information, as evidenced by the limited performance gains of newest SFDA methods.
Fan Wang +3 more
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Universal Multi-Source Domain Adaptation
CoRR, 2020 Unsupervised domain adaptation enables intelligent models to transfer knowledge from a labeled source domain to a similar but unlabeled target domain. Recent study reveals that knowledge can be transferred from one source domain to another unknown target domain, called Universal Domain Adaptation (UDA). However, in the real-world application, there are
Yueming Yin +3 more
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Multi-Source Survival Domain Adaptation
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the partial information held by censored cases. A good analysis can, for example, determine whether one medical treatment for a group of patients is better than another. With the
Ammar Shaker, Carolin Lawrence
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ANPERC-source/SEG_Annual: Database_Annual meetings v2.0
, 2020 The database of the SEG Annual meetings v2.0 This repository includes data for the words and phrases frequency of occurrence analysis "SEGgrams.sqlite" and the database "SEG_affiliations_data.sqlite" consisting of the industry companies and different ...
ANPERC-source
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Multi-source Multi-target Domain Adaptation Based on Evidence Theory [PDF]
Domain adaptation usually confronts the multiple source and multiple target domains issue. In such case, the reduction of distribution discrepancy across domains and the combination of information in diverse domains are two major subproblems.
Fan, Jinfu +3 more
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Combining multi-domain statistical machine translation models using automatic classifiers [PDF]
, 2010 This paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific corpora. The paper presents a novel approach for combining multiple domain-trained
Li, Baoli +5 more
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Time-domain computation of the response of composite layered anisotropic plates to a localized source [PDF]
, 2014 This paper describes how a modal approach in the time-domain can be suitable for calculating the elastodynamic field in a layered plate. This elastodynamic field is generated by impulsive sources located in a small region of a composite plate consisting ...
DUCASSE, Eric, DESCHAMPS, Marc
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