Results 281 to 290 of about 4,969,023 (298)

Subspace Distribution Adaptation Frameworks for Domain Adaptation

IEEE Transactions on Neural Networks and Learning Systems, 2020
Domain adaptation tries to adapt a model trained from a source domain to a different but related target domain. Currently, prevailing methods for domain adaptation rely on either instance reweighting or feature transformation. Unfortunately, instance reweighting has difficulty in estimating the sample weights as the dimension increases, whereas feature
Sentao Chen, Le Han, Xiaolan Liu, Zongyao He, Xiaowei Yang   +4 more
openaire   +2 more sources

Discriminative Manifold Distribution Alignment for Domain Adaptation

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023
Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and transferring knowledge from related source domains. In order to learn a discriminative and domain-invariant model, a critical step is to align source and target data
Siya Yao, Q. Kang, Mengchu Zhou, M. Rawa, A. Albeshri   +4 more
semanticscholar   +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   +5 more
openaire   +2 more sources

Partial Domain Adaptation in Remaining Useful Life Prediction With Incomplete Target Data

IEEE/ASME transactions on mechatronics
Intelligent machinery prognostics and health management (PHM) methods have been attracting growing attention in the past years, with the rapid development of the artificial intelligence algorithms.
Xiang Li, Wei Zhang, Xu Li, Hongshen Hao
semanticscholar   +1 more source

Gaussian Mixture Variational-Based Transformer Domain Adaptation Fault Diagnosis Method and Its Application in Bearing Fault Diagnosis

IEEE Transactions on Industrial Informatics
Unsupervised domain adaptation is widely used for fault diagnosis under variable working conditions. However, loss oscillation and slow convergence, which are caused by the dynamically varying alignment of targets during domain adaptation, are ignored ...
Yiyao An, Ke Zhang, Yi Chai, Zhiqin Zhu, Qie Liu   +4 more
semanticscholar   +1 more source

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, Yue Wu, Ke Lu
openaire   +1 more source

Shallow Domain Adaptation

2020
Supervised learning algorithms require sufficient amount of labeled training data for learning robust prediction models. The field of Transfer Learning (TL) (also known as knowledge transfer) deals with utilizing knowledge from data-rich auxiliary domains to learn a reliable predictor for the domain of interest. This chapter presents a condensed review
Sanatan Sukhija, Narayanan Chatapuram Krishnan   +1 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
openaire   +1 more source

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, Michael I. Jordan   +4 more
openaire   +1 more source

Deep Domain Adaptation

2018
Learning with limited labeled data is always a challenge in AI problems, and one of promising ways is transferring well-established source domain knowledge to the target domain, i.e., domain adaptation. Recent researches on transfer learning exploit deep structures for discriminative feature representation to tackle cross-domain disparity. However, few
Zhengming Ding, Handong Zhao, Yun Fu
openaire   +1 more source