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   Articles with "target domain" as a keyword



Modular domain-to-domain translation network

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recommendations! Published in 2019 at "Neural Computing and Applications"

DOI: 10.1007/s00521-019-04358-8

Abstract: Domain-to-domain translation methods map images from a source domain to corresponding images from a target domain. The two domains contain images from the same classes, but these images look different. Recent approaches use generative adversarial… read more here.

Keywords: domain translation; network; target domain; domain domain ... See more keywords
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Multistage transfer learning technique for classifying rare medical datasets

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recommendations! Published in 2021 at "Journal of Ambient Intelligence and Humanized Computing"

DOI: 10.1007/s12652-021-02989-1

Abstract: It is said that about 8% of the people across the world are impacted by different kinds of rare diseases. Identifying such rare diseases accurately is a challenging task, as they exhibit common symptoms that… read more here.

Keywords: domain dataset; transfer learning; medical datasets; domain ... See more keywords

Contextual Knowledge-Informed Deep Domain Generalization for Bearing Fault Diagnosis

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recommendations! Published in 2024 at "IEEE Access"

DOI: 10.1109/access.2024.3520624

Abstract: Reliable methods for bearing fault diagnosis are of great importance because they provide the possibility of preventing failures in machines. A significant challenge is developing solutions that can handle the variances in the data across… read more here.

Keywords: target domain; fault diagnosis; bearing fault; fault ... See more keywords

Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation

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recommendations! Published in 2021 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2021.3077271

Abstract: Accurate segmentation of the polyps from colonoscopy images provides useful information for the diagnosis and treatment of colorectal cancer. Despite deep learning methods advance automatic polyp segmentation, their performance often degrades when applied to new… read more here.

Keywords: polyp segmentation; segmentation; mutual prototype; domain ... See more keywords

Unsupervised Contrastive Learning-Based Single Domain Generalization Method for Intelligent Bearing Fault Diagnosis

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recommendations! Published in 2025 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2024.3507817

Abstract: In the field of fault diagnosis (FD), an increasing number of domain generalization (DG) methods are being employed to address domain shift issues. The vast majority of these methods focus on learning domain-invariant features from… read more here.

Keywords: target domain; domain samples; fault diagnosis; domain generalization ... See more keywords

A New Target Domain One-Class Learning Bearing Fault Detection Framework

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recommendations! Published in 2025 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2025.3635217

Abstract: Bearing fault detection is essential for machinery health management. An issue of existing work is that the network trained with a publicly available dataset (source domain) often has a large accuracy drop when applied to… read more here.

Keywords: target domain; fault detection; target; bearing fault ... See more keywords
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Multi-Source Collaborative Contrastive Learning for Decentralized Domain Adaptation

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recommendations! Published in 2023 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2022.3219893

Abstract: Unsupervised multi-source domain adaptation aims to obtain a model working well on the unlabeled target domain by reducing the domain gap between the labeled source domains and the unlabeled target domain. Considering the data privacy… read more here.

Keywords: domain adaptation; multi source; target domain; domain ... See more keywords

Pseudo-Label-Assisted Subdomain Adaptation for Hyperspectral Image Classification

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recommendations! Published in 2024 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2023.3330193

Abstract: Cross-domain classification of hyperspectral data is a critical challenge in remote sensing, especially when labels are unavailable in the target domain. Deep learning-based domain adaptation (DA) methods have been widely used in recent years. However,… read more here.

Keywords: adaptation; pseudo; target domain; label ... See more keywords

Cross-Attention With Conditional Matching for Multi-Target Domain Adaptation

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recommendations! Published in 2025 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2025.3578461

Abstract: As an emerging direction of machine learning, multi-target domain adaptation (MTDA) aims to address the challenges of adapting models to multiple target domains. However, existing studies often focus on single-target domain adaptation or fail to… read more here.

Keywords: adaptation; cross attention; target domain; multi target ... See more keywords

Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources

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recommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2020.3034344

Abstract: Cross-domain scene classification identifies scene categories by learning knowledge from a labeled data set (source domain) to an unlabeled data set (target domain), where the source data and the target data are sampled from different… read more here.

Keywords: cross domain; target domain; source; unknown categories ... See more keywords

Easy-to-Hard Structure for Remote Sensing Scene Classification in Multitarget Domain Adaptation

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recommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2023.3235886

Abstract: Multitarget domain adaptation (MTDA) is a transfer learning task that uses knowledge extracted from a labeled source domain to adapt across multiple unlabeled target domains. The MTDA setting is more complicated than the single-source-single-target domain… read more here.

Keywords: domain adaptation; target domain; domain; source ... See more keywords