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Unsupervised domain adaptation with progressive adaptation of subspaces

Pattern Recognition, 2022
Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy.
Songcan Chen
exaly   +4 more sources

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Robust domain adaptation

Annals of Mathematics and Artificial Intelligence, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yishay Mansour, Mariano Schain
openaire   +3 more sources

Adaptive Component Embedding for Domain Adaptation

IEEE Transactions on Cybernetics, 2021
Domain adaptation is suitable for transferring knowledge learned from one domain to a different but related domain. Considering the substantially large domain discrepancies, learning a more generalized feature representation is crucial for domain adaptation.
Mengmeng Jing, Jidong Zhao, Jingjing Li 0001, Lei Zhu 0002, Yang Yang 0002, Heng Tao Shen   +5 more
openaire   +2 more sources

Optimal Transport for Domain Adaptation [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system.
Rémi Flamary, Devis Tuia
exaly   +9 more sources

Universal Domain Adaptation

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Domain adaptation aims to transfer knowledge in the presence of the domain gap. Existing domain adaptation methods rely on rich prior knowledge about the relationship between the label sets of source and target domains, which greatly limits their application in the wild.
Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang 0001, Michael I. Jordan   +4 more
openaire   +2 more sources

Structured Domain Adaptation

IEEE Transactions on Circuits and Systems for Video Technology, 2017
In many real-world applications, labeled data are either expensive or too scarce to be used to train an accurate classifier. Therefore, it is worth exploring and often essential to make full use of existing resources. Domain adaptation is one of the most promising techniques of leveraging an existing well-labeled source domain and a limited labeled ...
Jingjing Li 0001, Yue Wu, Ke Lu 0001
openaire   +1 more source

Unsupervised Domain Adaptation via Domain-Adaptive Diffusion

IEEE Transactions on Image Processing
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, Qiuhong Ke, Arulmurugan Ambikapathi, Yasin Yazici, Yinjie Lei, Jun Liu 0036   +5 more
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Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation

ACM Transactions on Multimedia Computing, Communications, and Applications, 2022
Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain.
Jinfeng Li, Weifeng Liu 0001, Yicong Zhou, Jun Yu 0002, Dapeng Tao, Changsheng Xu   +5 more
openaire   +1 more source

Domain Adaptive Classification

2013 IEEE International Conference on Computer Vision, 2013
We propose an unsupervised domain adaptation method that exploits intrinsic compact structures of categories across different domains using binary attributes. Our method directly optimizes for classification in the target domain. The key insight is finding attributes that are discriminative across categories and predictable across domains. We achieve a
Fatemeh Mirrashed, Mohammad Rastegari
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Learning and Domain Adaptation

2009
Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises
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