Results 31 to 40 of about 3,036,262 (193)
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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FixBi:bridging domain spaces for unsupervised domain adaptation [PDF]
, 2021 Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from ...
Chang, Hyung Jin; id_orcid +7 more
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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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Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction [PDF]
, 2010 This paper demonstrates how unsupervised cross-lingual adaptation of HMM-based speech synthesis models may be performed without explicit knowledge of the adaptation data language.
Teemu Hirsimaki +5 more
core +2 more sources
CoRR, 2022 Recent works on unsupervised domain adaptation (UDA) focus on the selection of good pseudo-labels as surrogates for the missing labels in the target data. However, source domain bias that deteriorates the pseudo-labels can still exist since the shared network of the source and target domains are typically used for the pseudo-label selections.
Can Zhang 0007, Gim Hee Lee
openaire +3 more sources
IEEE Access, 2023 Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, geometry-based alignment methods, e.g., Orthogonal Procrustes Alignment (OPA), formed an important class ...
Kowshik Thopalli +3 more
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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
Aero-Engine Remaining Useful Life Prediction Based on Bi-Discrepancy Network
Sensors, 2023 Most unsupervised domain adaptation (UDA) methods align feature distributions across different domains through adversarial learning. However, many of them require introducing an auxiliary domain alignment model, which incurs additional computational ...
Nachuan Liu +3 more
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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
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Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer
Remote Sensing, 2022 With the development of deep learning, the performance of image semantic segmentation in remote sensing has been constantly improved. However, the performance usually degrades while testing on different datasets because of the domain gap.
Weitao Li +3 more
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