Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey [PDF]
Sensors, 2022 Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the ...
Yongjie Shi, Xianghua Ying, Jinfa Yang
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Adaptive Contrastive Learning with Label Consistency for Source Data Free Unsupervised Domain Adaptation [PDF]
Sensors, 2022 Unsupervised domain adaptation, which aims to alleviate the domain shift between source domain and target domain, has attracted extensive research interest; however, this is unlikely in practical application scenarios, which may be due to privacy issues ...
Xuejun Zhao +6 more
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Prototype-oriented class-conditional clustering transport for unsupervised domain adaptation [PDF]
Scientific ReportsUnsupervised domain adaptation (UDA) plays a vital role in machine learning to tackle the homogeneous data distribution scenario. While most previous studies have concentrated on between-domain transferability, they often neglect the rich within-domain ...
Liangda Yan, Jianwen Tao, Tao He
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Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation [PDF]
SensorsAccurately identifying tool wear states with variable cutting parameters can improve machining quality and efficiency. However, existing wear state recognition methods based on unsupervised domain adaptation mostly employ the knowledge transfer learning ...
Zhigang Cai +4 more
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Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder
Applied Sciences, 2022 Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains to assist target learning tasks. A critical aspect of unsupervised domain adaptation is the learning of more transferable and distinct feature ...
Yi Zhu, Xinke Zhou, Xindong Wu
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Unsupervised Domain Adaptation With Dense-Based Compaction for Hyperspectral Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Enormously hard work of label obtaining leads to the lack of enough annotated samples in the hyperspectral imagery (HSI). The mentioned reality inferred the unsupervised classification performance barely satisfactorily.
Chunyan Yu +4 more
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Unsupervised domain adaptation with post-adaptation labeled domain performance preservation
Machine Learning with Applications, 2022 Unsupervised domain adaptation is a machine learning-oriented application that aims to transfer knowledge learned from a seen (source) domain with labeled data to an unseen (target) domain with only unlabeled data.
Haidi Badr, Nayer Wanas, Magda Fayek
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Deep Adversarial-Reconstruction-Classification Networks for Unsupervised Domain Adaptation [PDF]
Jisuanji kexue yu tansuo, 2022 Recently, a new method of transfer learning called adversarial domain adaptation, embeds the idea of the generative adversarial networks (GAN) into the deep networks.
LIN Jiawei, WANG Shitong
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Bilateral co-transfer for unsupervised domain adaptation
Journal of Automation and Intelligence, 2023 Labeled data scarcity of an interested domain is often a serious problem in machine learning. Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested domain is a consensus.
Fuxiang Huang, Jingru Fu, Lei Zhang
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Unsupervised Adversarial Domain Adaptation for Agricultural Land Extraction of Remote Sensing Images
Remote Sensing, 2022 Agricultural land extraction is an essential technical means to promote sustainable agricultural development and modernization research. Existing supervised algorithms rely on many finely annotated remote-sensing images, which is both time-consuming and ...
Junbo Zhang +5 more
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