Zobrazeno 1 - 10
of 175
pro vyhledávání: '"Wang, Shunxin"'
Autor:
Wu, Boqian, Xiao, Qiao, Wang, Shunxin, Strisciuglio, Nicola, Pechenizkiy, Mykola, van Keulen, Maurice, Mocanu, Decebal Constantin, Mocanu, Elena
It is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy performance for the classification task. At the same time, Dense Train
Externí odkaz:
http://arxiv.org/abs/2410.03030
Computer vision models normally witness degraded performance when deployed in real-world scenarios, due to unexpected changes in inputs that were not accounted for during training. Data augmentation is commonly used to address this issue, as it aims
Externí odkaz:
http://arxiv.org/abs/2403.01944
Neural networks are prone to learn easy solutions from superficial statistics in the data, namely shortcut learning, which impairs generalization and robustness of models. We propose a data augmentation strategy, named DFM-X, that leverages knowledge
Externí odkaz:
http://arxiv.org/abs/2308.06622
Autor:
Mazilu, Ioana, Wang, Shunxin, Dummer, Sven, Veldhuis, Raymond, Brune, Christoph, Strisciuglio, Nicola
Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane, affecting the image quality for medical diagnosis and analysis of dis
Externí odkaz:
http://arxiv.org/abs/2307.15461
Frequency analysis is useful for understanding the mechanisms of representation learning in neural networks (NNs). Most research in this area focuses on the learning dynamics of NNs for regression tasks, while little for classification. This study em
Externí odkaz:
http://arxiv.org/abs/2307.09829
The performance of computer vision models are susceptible to unexpected changes in input images caused by sensor errors or extreme imaging environments, known as common corruptions (e.g. noise, blur, illumination changes). These corruptions can signi
Externí odkaz:
http://arxiv.org/abs/2305.06024
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Shao, Qiqi, Zhang, Yanan, Liu, Yanlei, Shang, Yongliang, Li, Si, Liu, Lin, Wang, Guoqiang, Zhou, Xu, Wang, Ping, Gao, Jinmin, Zhou, Jun, Zhang, Liangran, Wang, Shunxin
Publikováno v:
In Cell Reports 29 August 2023 42(8)
Publikováno v:
In Food Chemistry 30 June 2023 412
Autor:
Teng, Hui, Mi, Yani, Deng, Hongting, He, Yuanju, Wang, Shunxin, Ai, Chao, Cao, Hui, Chen, Lei
Publikováno v:
In Food Chemistry: X 30 March 2023 17