Results 71 to 80 of about 3,020,093 (290)
Transferable adversarial masked self-distillation for unsupervised domain adaptation
Complex & Intelligent Systems, 2023 Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to a related unlabeled target domain. Most existing works focus on minimizing the domain discrepancy to learn global domain-invariant representation using CNN ...
Yuelong Xia, Li-Jun Yun, Chengfu Yang
doaj +1 more source
Advanced Healthcare Materials, EarlyView.We developed a patient‐derived, functional microfluidic model of the diffuse midline glioma (DMG) blood–brain–tumor barrier (BBTB) comprised of endothelial cells, astrocytes, pericytes, and tumor cells. The system forms perfusable microvasculature, reveals the BBTB retains vascular integrity, identifies DMG‐specific transcriptomic changes distinct from
Kimberly R. Bennett +7 more
wiley +1 more source
TO-UGDA: target-oriented unsupervised graph domain adaptation
Scientific ReportsGraph domain adaptation (GDA) aims to address the challenge of limited label data in the target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for different-level (node-level, graph-level) adaptation tasks exhibit variations in ...
Zhuo Zeng +4 more
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Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing
IEEE AccessIn semantic segmentation, current deep convolutional neural networks rely heavily on extensive data to achieve superior segmentation results. However, these deep models have poor generalization ability across different domain datasets.
Xiaojing Li, Wei Zhou, Mingjian Jiang
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Advanced Materials, EarlyView.Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam +6 more
wiley +1 more source
Topic modeling-based domain adaptation for system combination [PDF]
, 2012 This paper gives the system description of the domain adaptation team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in ...
Okita, Tsuyoshi +2 more
core +2 more sources
IEEE Access, 2019 Deep convolutional networks have demonstrated state-of-the-art performance on various challenging medical image processing tasks. Leveraging images from different modalities for the same analysis task holds large clinical benefits.
Qi Dou +6 more
doaj +1 more source
Multimodal Domain Adaptation for Point Cloud Semantic Segmentation [PDF]
, 2023 openSemantic segmentation, thanks to multimodal datasets, can be made more reliable and accurate by mixing heterogeneous data, e.g. RGB images with LIDAR sequences or depth maps.
NICOLETTI, GIANPIETRO
core
Advanced Materials, EarlyView.The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
Unsupervised multi-target domain adaptation for deforestation detection in tropical rainforest [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesGeographic variability of the classes of interest, differences in sensor characteristics and changes in atmospheric conditions during image acquisition, among other factors, make it challenging to use a pre-trained deep learning classifier on new remote ...
M. X. Ortega Adarme +5 more
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