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pro vyhledávání: '"joint training"'
The current trend in computer vision is to utilize one universal model to address all various tasks. Achieving such a universal model inevitably requires incorporating multi-domain data for joint training to learn across multiple problem scenarios. I
Externí odkaz:
http://arxiv.org/abs/2411.01584
Autor:
Li, Zhaohui, Passonneau, Rebecca J.
Classifier models are prevalent in natural language processing (NLP), often with high accuracy. Yet in real world settings, human-in-the-loop systems can foster trust in model outputs and even higher performance. Selective Prediction (SP) methods det
Externí odkaz:
http://arxiv.org/abs/2410.24029
Autor:
Pálka, Petr, Landini, Federico, Klement, Dominik, Diez, Mireia, Silnova, Anna, Delcroix, Marc, Burget, Lukáš
In spite of the popularity of end-to-end diarization systems nowadays, modular systems comprised of voice activity detection (VAD), speaker embedding extraction plus clustering, and overlapped speech detection (OSD) plus handling still attain competi
Externí odkaz:
http://arxiv.org/abs/2411.02165
Severity level estimation is a crucial task in medical image diagnosis. However, accurately assigning severity class labels to individual images is very costly and challenging. Consequently, the attached labels tend to be noisy. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2410.21885
Autor:
Yang, Xingyao1 (AUTHOR) yangxy@xju.edu.cn, Dang, Zibo1 (AUTHOR), Yu, Jiong1 (AUTHOR), Zhong, Zhiqiang1 (AUTHOR), Chang, Mengxue1 (AUTHOR), Zhang, Zulian2 (AUTHOR)
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 2024, Vol. 46 Issue 1, p941-953. 13p.
Akademický článek
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Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accur
Externí odkaz:
http://arxiv.org/abs/2403.19955
A major drawback of supervised speech separation (SSep) systems is their reliance on synthetic data, leading to poor real-world generalization. Mixture invariant training (MixIT) was proposed as an unsupervised alternative that uses real recordings,
Externí odkaz:
http://arxiv.org/abs/2403.02288
Autor:
PÎNZARIU, Sorin Gheorghe sorinpinz@yahoo.com, NEAG, Mihai Marcel mmneag@yahoo.com, PÎNZARIU, Andra Ioana1 andra.pinzariu21@gmail.com
Publikováno v:
Buletin Stiintific. 2024, Vol. 29 Issue 1, p111-117. 7p.