Results 61 to 70 of about 2,905 (180)

A Fine-Grained Unsupervised Domain Adaptation Framework for Semantic Segmentation of Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Unsupervised domain adaptation (UDA) aims at adapting a model from the source domain to the target domain by tackling the issue of domain shift.
Luhan Wang, Pengfeng Xiao, Xueliang Zhang, Xinyang Chen   +3 more
doaj   +1 more source

Pseudo-class distribution guided multi-view unsupervised domain adaptation for hyperspectral image classification

International Journal of Applied Earth Observations and Geoinformation
Unsupervised domain adaptation (UDA) has made great progress in cross-scene hyperspectral image (HSI) classification. Existing methods focus on aligning the distribution of source domain (SD) and target domain (TD).
Jingpeng Gao, Xiangyu Ji, Geng Chen, Yuhang Huang, Fang Ye   +4 more
doaj   +1 more source

Multi-component Image Translation for Deep Domain Generalization

, 2018
Domain adaption (DA) and domain generalization (DG) are two closely related methods which are both concerned with the task of assigning labels to an unlabeled data set. The only dissimilarity between these approaches is that DA can access the target data
Baktashmotlagh, Mahsa, Fookes, Clinton, Rahman, Mohammad Mahfujur, Sridharan, Sridha   +3 more
core   +1 more source

Model adaptation via credible local context representation

CAAI Transactions on Intelligence Technology, Volume 10, Issue 3, Page 638-651, June 2025.
Abstract Conventional model transfer techniques, requiring the labelled source data, are not applicable in the privacy‐protected medical fields. For the challenging scenarios, recent source data‐free domain adaptation (SFDA) has become a mainstream solution but losing focus on the inter‐sample class information. This paper proposes a new Credible Local
Song Tang, Wenxin Su, Yan Yang, Lijuan Chen, Mao Ye   +4 more
wiley   +1 more source

Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning

Engineering
Automatic identification of microseismic (MS) signals is crucial for early disaster warning in deep underground engineering. However, three major challenges remain for practical deployment, namely limited resources, severe noise interference, and data ...
Dingran Song, Feng Dai, Yi Liu, Hao Tan, Mingdong Wei   +4 more
doaj   +1 more source

Synthetic Source Universal Domain Adaptation through Contrastive Learning

Sensors, 2021
Universal domain adaptation (UDA) is a crucial research topic for efficient deep learning model training using data from various imaging sensors. However, its development is affected by unlabeled target data. Moreover, the nonexistence of prior knowledge
Jungchan Cho
doaj   +1 more source

High‐Throughput Robotic Phenotyping for Quantifying Tomato Disease Severity Enabled by Synthetic Data and Domain‐Adaptive Semantic Segmentation

Journal of Field Robotics, Volume 42, Issue 3, Page 657-678, May 2025.
ABSTRACT Plant diseases cause an annual global crop loss of 20%–40%, leading to estimated economic losses of 30–50 billion dollars. Tomatoes are susceptible to more than 200 diseases. Breeding disease‐resistant cultivars is more cost‐effective and environmentally sustainable than the frequent use of pesticides.
Weilong He, Xingjian Li, Zhenghua Zhang, Yuxi Chen, Jianbo Zhang, Dilip R. Panthee, Inga Meadows, Lirong Xiang   +7 more
wiley   +1 more source

Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 1, March 2025.
Abstract Retrogressive Thaw Slumps (RTSs), a form of thermokarst hazards, pose risks to hydrological and ecological environments and the safety of the Qinghai‐Tibet Engineering Corridor. We still lack the knowledge about the geographic locations of RTSs and their dynamically changing spatial margins.
Yiling Lin, Xie Hu, Haoyu Lu, Fujun Niu, Gengnian Liu, Lingcao Huang, Shanghang Zhang, Jifu Liu, Yunhuai Liu   +8 more
wiley   +1 more source

Unsupervised SAR Fine-Grained Ship Classification via Spherical Metric Refinement With Deep Subdomain Adaptation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unsupervised domain adaptation (UDA) is a promising method for addressing the problem of SAR fine-grained ship classification in target domain with no labeled data available by leveraging a large number of labeled samples from source domains.
Zhichao Han, Haitao Lang
doaj   +1 more source

Metric‐guided class‐level alignment for domain adaptation

IET Computer Vision, Volume 19, Issue 1, January/December 2025.
The study of class‐level domain adaptation, which aims to precisely match the distributions of different domains, has garnered attention in recent times. However, existing investigations into class‐level alignment frequently align domain features either directly on or in close proximity to classification boundaries, resulting in the creation of ...
Xiaoshun Wang, Yunhan Li
wiley   +1 more source