JOLT
发表于 2025-3-21 17:35:59
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obsolete
发表于 2025-3-21 23:54:35
n models across different application domains. Extensive experiments were carried using different established methods: EAST, CRAFT, Tessaract and Ensembles applied to various publicly available datasets. The generalisation performance of the models was evaluated and compared using precision, recall
OPINE
发表于 2025-3-22 02:32:21
Kenneth J. Koval,Joseph D. Zuckermanificantly sparser modifications than other approaches, we achieve comparable or better performance on all metrics. Moreover, we demonstrate that our approach predominantly modifies salient time steps and features, leaving non-salient inputs untouched.
Type-1-Diabetes
发表于 2025-3-22 06:46:54
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元音
发表于 2025-3-22 08:58:10
Kenneth J. Koval,Joseph D. Zuckermantatistical features, and the SR-Module is composed of our proposed DUSCMAnet, a lightweight SR network. After classifying, a majority of sub-images will pass through lighter networks, thus the computational cost can be significantly reduced. Experiments show that our DUSCMAnet is superior to the exi
Nonconformist
发表于 2025-3-22 13:49:35
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厌烦
发表于 2025-3-22 18:17:09
Kenneth J. Koval,Joseph D. Zuckermanontent, . employs deep neural networks to learn their representations automatically. Moreover, we propose a strategy to generate portfolios by utilizing prediction results obtained by .. Extensive data analysis and experiments show that . can bring us high-yield portfolios.
gnarled
发表于 2025-3-22 22:41:44
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Root494
发表于 2025-3-23 02:59:51
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Desert
发表于 2025-3-23 07:54:37
Kenneth J. Koval,Joseph D. Zuckermanin-specific and domain-invariant features is enlarged, which promotes RPN feature to contain more domain-invariant information. Furthermore, we propose dynamic weighted adversarial training to alleviate the unstable training caused by adversarial learning. We conduct extensive experiments on multipl