Results 21 to 30 of about 2,905 (180)
A semi-supervised domain adaptation method with scale-aware and global-local fusion for abdominal multi-organ segmentation. [PDF]
J Appl Clin Med PhysAbstract Background Abdominal multi‐organ segmentation remains a challenging task. Semi‐supervised domain adaptation (SSDA) has emerged as an innovative solution. However, SSDA frameworks based on UNet struggle to capture multi‐scale and global information.
Han K, Lou Q, Lu F.
europepmc +2 more sources
Unsupervised domain adaptation with post-adaptation labeled domain performance preservation
Machine Learning with Applications, 2022 Unsupervised domain adaptation is a machine learning-oriented application that aims to transfer knowledge learned from a seen (source) domain with labeled data to an unseen (target) domain with only unlabeled data.
Haidi Badr, Nayer Wanas, Magda Fayek
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Cross Domain Mean Approximation for Unsupervised Domain Adaptation
IEEE Access, 2020 Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled source domain to help the task of target domain with the unlabeled data. It is a key step for UDA to minimize the cross-domain distribution divergence. In this paper, we
Shaofei Zang +4 more
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Electronic Research Archive, 2023 Unsupervised domain adaptation (UDA) is an emerging research topic in the field of machine learning and pattern recognition, which aims to help the learning of unlabeled target domain by transferring knowledge from the source domain.
Qing Tian +4 more
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International Journal of Applied Earth Observations and Geoinformation, 2022 Despite its success, deep learning in land cover mapping requires a massive amount of pixel-wise labeled images. It typically assumes that the training and test scenes are similar in data distribution.
Wei Liu +5 more
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Class reconstruction driven adversarial domain adaptation for hyperspectral image classification [PDF]
, 2019 We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training ...
AL Neuenschwander +8 more
core +1 more source
Animals, 2023 Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior.
Seong-Ho Ahn, Seeun Kim, Dong-Hwa Jeong
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Unsupervised domain adaptation for lip reading based on cross-modal knowledge distillation
EURASIP Journal on Audio, Speech, and Music Processing, 2021 We present an unsupervised domain adaptation (UDA) method for a lip-reading model that is an image-based speech recognition model. Most of conventional UDA methods cannot be applied when the adaptation data consists of an unknown class, such as out-of ...
Yuki Takashima +6 more
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Sensors, 2023 Unsupervised domain adaptation (UDA) is a transfer learning technique utilized in deep learning. UDA aims to reduce the distribution gap between labeled source and unlabeled target domains by adapting a model through fine-tuning.
Parth Goel, Amit Ganatra
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Background-Aware Domain Adaptation for Plant Counting
Frontiers in Plant Science, 2022 Deep learning-based object counting models have recently been considered preferable choices for plant counting. However, the performance of these data-driven methods would probably deteriorate when a discrepancy exists between the training and testing ...
Min Shi, Xing-Yi Li, Hao Lu, Zhi-Guo Cao
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