Zobrazeno 1 - 10
of 17 745
pro vyhledávání: '"Domain adaptation"'
While speech emotion recognition (SER) research has made significant progress, achieving generalization across various corpora continues to pose a problem. We propose a novel domain adaptation technique that embodies a multitask framework with SER as
Externí odkaz:
http://arxiv.org/abs/2310.04703
Publikováno v:
水下无人系统学报, Vol 32, Iss 5, Pp 846-854 (2024)
Underwater target detection is often more susceptible to domain shift and reduced detection accuracy. In response to this phenomenon, this article proposed a domain-adaptive underwater target detection method based on graph-induced prototype alignmen
Externí odkaz:
https://doaj.org/article/ff80c1935cc447408f7a0f9096c82a71
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-26 (2024)
Abstract Mechanical equipment is a vital foundational support for promoting national economic development and is widely utilized in key sectors such as aerospace, shipping, construction machinery, energy, petrochemicals, and robotics. With the advanc
Externí odkaz:
https://doaj.org/article/5cf417fab61845d5b6f7b9ddea43da9c
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Kidney cancer is a serious malignant disease, and early diagnosis along with precise segmentation are crucial for effective treatment. However, due to the scarcity of labelled medical image data, the development of the intelligent diagnosis
Externí odkaz:
https://doaj.org/article/c4e6ac916dd548ca89d29921263c84cf
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, with applications in environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in p
Externí odkaz:
https://doaj.org/article/f0300b41864149db91b92d6107cd85ae
Autor:
Aaron P. Fjeldsted, Tyler J. Morrow, Clayton D. Scott, Yilun Zhu, Darren E. Holland, Azaree T. Lintereur, Douglas E. Wolfe
Publikováno v:
Journal of Nuclear Engineering, Vol 5, Iss 3, Pp 373-401 (2024)
Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to t
Externí odkaz:
https://doaj.org/article/57e992fbfa744171b3bfb3b0922ed131
Publikováno v:
Geo-spatial Information Science, Pp 1-15 (2024)
Hyperspectral datasets captured by airborne are one of the important data sources in the field of hyperspectral image fusion. However, the limited number of data samples often hinders the optimal performance of deep learning-based methods on these da
Externí odkaz:
https://doaj.org/article/2c261a1c2e244549a67f6543f2382f7c
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 9, Pp 2384-2394 (2024)
Existing 3D human pose estimators perform well on a single dataset but are limited by the single pose structure of the training data, resulting in insufficient generalization to cross-domain experiments. Existing methods mitigate this deficiency by i
Externí odkaz:
https://doaj.org/article/4232f6f5ba3e4bde8d503f707ee1ddc6
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 9, Pp 2436-2448 (2024)
The main purpose of unbiased cross-domain object detection is to utilize the knowledge of the source domain to the maximum extent through knowledge distillation, and reduce the cross-domain gap of the model through domain adaptation. However, the pse
Externí odkaz:
https://doaj.org/article/69cc600e7a07402fb07d35f75b26677e
Publikováno v:
Geo-spatial Information Science, Pp 1-18 (2024)
To enhance the adaptability and application capability of the cloud detection model in different remote sensing satellite domains, unsupervised domain adaptation methods are employed to improve the model’s robustness and transferability. However, c
Externí odkaz:
https://doaj.org/article/c6300786f6af4475a1884f901bd73b58