Results 51 to 60 of about 3,036,262 (193)

Backpropagation Network‐Based Contrastive Learning for Unsupervised Domain Adaptation

Engineering Reports, Volume 8, Issue 6, June 2026.
Unsupervised domain adaptation for contractive learning. ABSTRACT This study introduces a new method for domain adaptation for image classification tasks that aims to improve the model's performance on a target domain after being trained on a source domain.
Yushui Xiao, Yong Huang, Yujie Li
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

Accelerating Full‐Wave Antenna Optimization: An Adaptive Surrogate‐Assisted Differential Evolution Framework

Engineering Reports, Volume 8, Issue 5, May 2026.
An adaptive surrogate‐assisted differential evolution framework integrates machine learning‐based surrogate modeling with selective full‐wave electromagnetic validation to accelerate antenna optimization. A CST–Python workflow combines Latin‐hypercube sampling, cross‐validated model selection, and iterative dataset refinement to guide the search toward
Muhammad Farooq, Shahid Bashir, Mohammad Haseeb Zafar, Ruhul Amin Khalil   +3 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

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

Advances in Artificial Intelligence‐Based Liver‐Related Semantic Segmentation Techniques and Applications Using CT Imaging

Cancer Medicine, Volume 15, Issue 4, April 2026.
This review summarizes clinical and technical advancements in AI‐based semantic segmentation for liver organ, tumors, and vasculature on CT imaging. It highlights key applications in surgical planning and disease evaluation while discussing critical challenges and strategies for real‐world clinical deployment and multimodal integration.
Jun Pu, Xuan Wang, Liang Zhu, Jie Pan
wiley   +1 more source

SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
10 pages, 7 figures, CVPR ...
Hyungseob Shin, Hyeongyu Kim, Sewon Kim, Yohan Jun, Taejoon Eo, Dosik Hwang   +5 more
openaire   +3 more sources

UAV-based Unsupervised Domain Adaptation for Road Extraction [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Despite advances in Deep Learning (DL) for road extraction, this task remains challenging. First, domain shifts in data distribution hinder the inference of pre-trained models to new areas, leading to a drop in classification accuracy.
G. R. Collegio, A. G. Guimarães Filho, A. P. Dal Poz, A. Habib   +3 more
doaj   +1 more source

Single‐Cell and Spatial Omics: Methods and Applications

MedComm, Volume 7, Issue 4, April 2026.
Systematically summarized the breakthrough sequencing technologies and computational methods for single‐cell and spatial omics across multiple omics layers, including genome, epigenome, transcriptome, proteome, and metabolome. State‐of‐the‐art methods for multi‐omics integration, cross‐modal integration, and cross‐scale integration were reviewed, with ...
Xiaoping Cen, Xiaolan Huang, Enjin Deng, Xue Gong, Na Tan, Jifeng Ye, Yin Wang, Roland Eils, Qun Luo, Yixue Li, Fangfang Qu   +10 more
wiley   +1 more source

On Fine-tuned Deep Features for Unsupervised Domain Adaptation [PDF]

, 2023
Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptation ...
Wang, Q., Breckon, T.P., Meng, F.
core   +1 more source