1 code implementation • 19 Jan 2024 • Cunhang Fan, Yujie Chen, Jun Xue, Yonghui Kong, JianHua Tao, Zhao Lv
This paper proposes a progressive distillation method based on masked generation features for KGC task, aiming to significantly reduce the complexity of pre-trained models.
no code implementations • 22 Oct 2023 • Shiang Hu, Jie Ruan, Juan Hou, Pedro Antonio Valdes-Sosa, Zhao Lv
Then, the impacts on postprocessing were quantified by the deviation caused by the IPE or EPE from the CE as to the 4 temporal statistics, the multichannel power, the cross spectra, the dispersion of source imaging, and the properties of scalp EEG network.
no code implementations • 7 Sep 2023 • Cunhang Fan, Hongyu Zhang, Wei Huang, Jun Xue, JianHua Tao, Jiangyan Yi, Zhao Lv, Xiaopei Wu
Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals.
no code implementations • 27 Jun 2023 • Shunbo Dong, Jun Xue, Cunhang Fan, Kang Zhu, Yujie Chen, Zhao Lv
The main purpose of this system is to improve the model's ability to learn precise forgery information for FSD task in low-quality scenarios.
no code implementations • 2 Mar 2023 • Jun Xue, Cunhang Fan, Jiangyan Yi, Chenglong Wang, Zhengqi Wen, Dan Zhang, Zhao Lv
To address this problem, we propose using the deepest network instruct shallow network for enhancing shallow networks.
no code implementations • 2 Aug 2022 • Jun Xue, Cunhang Fan, Zhao Lv, JianHua Tao, Jiangyan Yi, Chengshi Zheng, Zhengqi Wen, Minmin Yuan, Shegang Shao
Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately.