Results 51 to 60 of about 2,905 (180)

Dual Frequency Side Scan Sonar Image Fusion for Deep‐Learning Based Underwater Target Detection

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
Traditional single‐frequency side‐scan sonar faces trade‐offs between imaging resolution and detection range, along with issues such as speckle noise and target–shadow coupling. To address these, this study proposes D2FNet, a dual‐domain fusion model integrating three key modules, which—tested on a new dataset of over 9000 paired sea‐trial images ...
Jiajun Xian, Liming Li, Fan Zhang, Yingying Wu, Weilin Zang, Lingji Xu, Hongxun Yao, Adam Rushworth, Johannes Michael Herrmann, Sanming Song   +9 more
wiley   +1 more source

Domain‐specific feature recalibration and alignment for multi‐source unsupervised domain adaptation

IET Computer Vision, 2023
Traditional unsupervised domain adaptation (UDA) usually assumes that the source domain has labels and the target domain has no labels. In a real environment, labelled source domain data usually comes from multiple different distributions. To handle this
Mengzhu Wang, Dingyao Chen, Fangzhou Tan, Tianyi Liang, Long Lan, Xiang Zhang, Zhigang Luo   +6 more
doaj   +1 more source

Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

Remote Sensing, 2021
Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data.
Chenhui Ma, Dexuan Sha, Xiaodong Mu
doaj   +1 more source

Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? A Metric for Unsupervised Evaluation of Domain Adaptation

, 2023
Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain. However, in real-world scenarios, the absence of target-domain labels makes it challenging to evaluate the performance of ...
Qian, Hanjie, Xie, Lihua, Xu, Yuecong, Yang, Jianfei   +3 more
core  

TF‐MEET: A Transferable Fusion Multi‐Band Transformer for Cross‐Session EEG Decoding

CAAI Transactions on Intelligence Technology, Volume 10, Issue 6, Page 1799-1812, December 2025.
ABSTRACT Electroencephalography (EEG) is a widely used neuroimaging technique for decoding brain states. Transformer is gaining attention in EEG signal decoding due to its powerful ability to capture global features. However, relying solely on a single feature extracted by the traditional transformer model to address the domain shift problem caused by ...
Qilong Yuan, Enze Shi, Di Zhu, Xiaoshan Zhang, Kui Zhao, Dingwen Zhang, Tianming Liu, Shu Zhang   +7 more
wiley   +1 more source

The SYNTHIDIA Dataset: Synthetic Insulator Defect Imaging and Annotation

High Voltage, Volume 10, Issue 6, Page 1496-1508, December 2025.
ABSTRACT Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply. However, the development of deep‐learning‐based insulator defect detection is hindered by the scarcity of comprehensive, high‐quality datasets for insulator defects.
Qingzhen Liu, Yadong Liu, Yingjie Yan, Qian Jiang, Xiuchen Jiang   +4 more
wiley   +1 more source

Multiscale Change Detection Domain Adaptation Model Based on Illumination–Reflection Decoupling

Remote Sensing
In the change detection (CD) task, the substantial variation in feature distributions across different CD datasets significantly limits the reusability of supervised CD models.
Rongbo Fan, Jialin Xie, Jianhua Yang, Zenglin Hong, Yuqi Xu, Hong Hou   +5 more
doaj   +1 more source

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

, 2018
In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the ...
Harada, Tatsuya, Saito, Kuniaki, Ushiku, Yoshitaka, Watanabe, Kohei   +3 more
core   +1 more source

Efficient unsupervised domain adaptation for crack segmentation with interpretable Fourier– Morphology blending and Uncertainty‐guided self‐training

Computer-Aided Civil and Infrastructure Engineering, Volume 40, Issue 29, Page 5790-5807, 9 December 2025.
Abstract Automated crack segmentation models are vital for infrastructure monitoring but fail when deployed in new domains. Overcoming this domain shift without costly re‐annotation is vital. This paper presents a novel unsupervised domain adaptation framework that uniquely integrates Fourier‐based style transfer with targeted morphological operators ...
Saheli Bhattacharya, Chen Zhang, Dhanada K. Mishra, Matthew M. F. Yuen, Jize Zhang   +4 more
wiley   +1 more source

Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy

EURASIP Journal on Advances in Signal Processing
In the existing domain adaptation-based bearing fault diagnosis methods, the data difference between the source domain and the target domain is not obvious.
Cuixiang Wang, Shengkai Wu, Xing Shao
doaj   +1 more source