Results 11 to 20 of about 389,599 (264)
Adversarial Multitask Learning for Domain Adaptation Through Domain Adapter
IEEE AccessThis study presents a technique called Adversarial Multitask Learning (AML) to enhance the effectiveness of domain adaptation methods in practical applications, which are currently highly sought after. The proposed approach addresses the challenges posed
Hidayaturrahman +3 more
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Unsupervised Domain Adaptation with Adapter
CoRR, 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 ...
Rongsheng Zhang +3 more
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Contrastive Domain Adaptation [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021 10 pages, 6 figures, 5 ...
Mamatha Thota, Georgios Leontidis
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Domain-Augmented Domain Adaptation
CoRR, 2022 Unsupervised domain adaptation (UDA) enables knowledge transfer from the labelled source domain to the unlabeled target domain by reducing the cross-domain discrepancy. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies.
Qiuhao Zeng, Tianze Luo, Boyu Wang 0004
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Unsupervised Domain Adaptation via Domain-Adaptive Diffusion
CoRR, 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 ...
Duo Peng +3 more
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Generalized Domain Adaptation [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Accepted by CVPR 2021.
Yu Mitsuzumi +3 more
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Findings of the Association for Computational Linguistics: ACL 2022, 2022 Findings of ACL (2022)
Junshen K. Chen +2 more
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Self-Adaptation for Unsupervised Domain Adaptation [PDF]
Proceedings - Natural Language Processing in a Deep Learning World, 2019 Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches. Using the labelled data from the source domain, we first learn a projection that maximises the distance among ...
Cui, Xia, Bollegala, Danushka
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Unsupervised Domain Adaptation for 3D Point Clouds by Searched Transformations
IEEE Access, 2022 Input-level domain adaptation reduces the burden of a neural encoder without supervision by reducing the domain gap at the input level. Input-level domain adaptation is widely employed in 2D visual domain, e.g., images and videos, but is not utilized for
Dongmin Kang +3 more
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Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source ...
Jichang Li +3 more
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