Results 21 to 30 of about 12,484 (262)
Unsupervised Domain Adaptation Based on Correlation Maximization
IEEE Access, 2021 This research proposes a novel unsupervised domain adaptation algorithm for cross-domain visual recognition. Distance Correlation-based Domain Adaptation or DCDA algorithm is developed by a correlation measure, called distance correlation.
Lida Abdi, Sattar Hashemi
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Unsupervised Domain Adaptation Based on Style Aware [PDF]
Jisuanji kexue, 2022 In recent years,neural machine translation has made significant progress in translation quality,but it relies on parallel bilingual sentence pairs heavily during the training process.However,parallel resources are scarce for the e-commerce domain,in ...
NING Qiu-yi, SHI Xiao-jing, DUAN Xiang-yu, ZHANG Min
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Unsupervised Domain Adaptation with Adapter
, 2021 Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from various domains. However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to ...
Zhang, Rongsheng +3 more
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Multi-Source Attention for Unsupervised Domain Adaptation [PDF]
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, 2020 Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain. Often it is difficult to find a suitable single source to adapt from, and one must consider multiple sources.
Cui, Xia, Bollegala, Danushka
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Unsupervised Domain Adaptation for SAR Target Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Recent years have witnessed great progress in synthetic aperture radar (SAR) target detection methods based on deep learning. However, these methods generally assume the training data and test data obey the same distribution, which does not always hold ...
Yu Shi, Lan Du, Yuchen Guo
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Unsupervised Domain Adaptation via Domain-Adaptive Diffusion
, 2023 Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task ...
Peng, Duo +3 more
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BOOSTED UNSUPERVISED MULTI-SOURCE SELECTION FOR DOMAIN ADAPTATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training data ...
K. Vogt +4 more
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CUDA: Contradistinguisher for Unsupervised Domain Adaptation [PDF]
2019 IEEE International Conference on Data Mining (ICDM), 2019 International Conference on Data Mining, ICDM ...
Balgi, Sourabh, Dukkipati, Ambedkar
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Cross-Domain Error Minimization for Unsupervised Domain Adaptation [PDF]
, 2021 Accepted by DASFAA ...
Du, Yuntao +4 more
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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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