Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Weilian Zhou"'
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 61:1-19
Publikováno v:
Neurocomputing. 487:257-268
The depth of the deep neural network (DNN) refers to the number of hidden layers between the input and output layers of an artificial neural network. It usually indicates a certain degree of complexity of the computational cost (parameters and floati
Publikováno v:
Complex & Intelligent Systems. 8:3395-3407
Abstract Various deep neural network architectures (DNNs) maintain massive vital records in computer vision. While drawing attention worldwide, the design of the overall structure lacks general guidance. Based on the relationship between DNN design a
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-18
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Publikováno v:
2022 26th International Conference on Pattern Recognition (ICPR).
Autor:
Xingchang Tang, Chuan Kuang, Weilian Zhou, Kexuan Chen, Jiankang Huang, Xiaoquan Yv, Canglong Wang, Peiqing La
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON).
Publikováno v:
ICASSP
Most structures of deep neural networks (DNN) are with a fixed complexity of both computational cost (parameters and FLOPs) and the expressiveness. In this work, we experimentally investigate the effectiveness of using neural ordinary differential eq