Zobrazeno 1 - 10
of 2 396
pro vyhledávání: '"Self-Training"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1926-1945 (2025)
Semantic segmentation techniques for remote sensing scene understanding have significantly advanced, enhancing the refined Earth observation. However, most methods highly depend on extensive annotated data, leading to performance deterioration in com
Externí odkaz:
https://doaj.org/article/e034f4d7eac74ff18fe35f7505e99c44
Publikováno v:
Taiyuan Ligong Daxue xuebao, Vol 55, Iss 6, Pp 1053-1062 (2024)
[Purposes] The research of precise location of underground personnel in coal mine is of great significance to protect their life safety. The ultra-wideband signal is susceptible to non-lineof- sight (NLOS) interference, which will seriously affects t
Externí odkaz:
https://doaj.org/article/1d132eeb5d6e43bfb58feaef0cbfdedd
Publikováno v:
In Energy 30 December 2024 313
Autor:
Huang, Yingchao a, ⁎, Hussein, Amina E. b, Wang, Xin a, Bais, Abdul c, Yao, Shanshan d, Wilder, Tanis a
Publikováno v:
In Intelligent Systems with Applications March 2025 25
Publikováno v:
Tongxin xuebao, Vol 45, Pp 65-72 (2024)
To enhance the performance of neural machine translation (NMT) and ameliorate the detrimental impact of high uncertainty in monolingual data during the self-training process, a self-training NMT model based on priority sampling was proposed. Initiall
Externí odkaz:
https://doaj.org/article/9ca8a33421464f068d2fb35f28d15fc8
Autor:
Florent Forest, Olga Fink
Publikováno v:
Sensors, Vol 24, Iss 23, p 7539 (2024)
Intelligent fault diagnosis (IFD) based on deep learning can achieve high accuracy from raw condition monitoring signals. However, models usually perform well on the training distribution only, and experience severe performance drops when applied to
Externí odkaz:
https://doaj.org/article/55dfa3552bae4e9ba1be700f14661613
Autor:
Manu Ramesh, Amy R. Reibman
Publikováno v:
Sensors, Vol 24, Iss 23, p 7680 (2024)
We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by
Externí odkaz:
https://doaj.org/article/fe9d400080c84dd6b11dde803fcfe5a2
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 20265-20283 (2024)
This article proposes an unsupervised domain adaptation (UDA) method by transferring knowledge from rich labeled optical domain to unlabeled synthetic aperture radar (SAR) domain, tackling the issue that current deep-learning-based SAR target detecti
Externí odkaz:
https://doaj.org/article/df64089c38624c62b8063cfa99d5e86a
Publikováno v:
IEEE Access, Vol 12, Pp 110418-110431 (2024)
In the real world, there are only a small amount of data with labels. To make full use of the potential structural information of unlabeled data to train a better classifier, researchers have proposed many semi-supervised learning algorithms. Among t
Externí odkaz:
https://doaj.org/article/96ad1915e58b4ea8880166c48aa568f5
Autor:
Michela Venturini, Fateme Nateghi Haredasht, Frantisek Sabovcik, Robert J. H. Miller, Tatiana Kuznetsova, Celine Vens
Publikováno v:
IEEE Access, Vol 12, Pp 89754-89762 (2024)
Heart transplantation is a life-saving procedure for children affected by end-stage heart failure. However, despite recent improvements in long-term outcomes, 1-year post-transplantation mortality has remained relatively high. Accurate prediction of
Externí odkaz:
https://doaj.org/article/7539fd68637149e0a05d4fb4be5fe21c