Search Results for author: Guangshe Zhao

Found 7 papers, 1 papers with code

Inherent Redundancy in Spiking Neural Networks

1 code implementation16 Aug 2023 Man Yao, Jiakui Hu, Guangshe Zhao, Yaoyuan Wang, Ziyang Zhang, Bo Xu, Guoqi Li

In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs.

Attention Spiking Neural Networks

no code implementations28 Sep 2022 Man Yao, Guangshe Zhao, Hengyu Zhang, Yifan Hu, Lei Deng, Yonghong Tian, Bo Xu, Guoqi Li

On ImageNet-1K, we achieve top-1 accuracy of 75. 92% and 77. 08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs.

Action Recognition Image Classification

Temporal-wise Attention Spiking Neural Networks for Event Streams Classification

no code implementations ICCV 2021 Man Yao, Huanhuan Gao, Guangshe Zhao, Dingheng Wang, Yihan Lin, ZhaoXu Yang, Guoqi Li

However, when aggregating individual events into frames with a new higher temporal resolution, existing SNN models do not attach importance to that the serial frames have different signal-to-noise ratios since event streams are sparse and non-uniform.

Classification Gesture Recognition +1

Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs

no code implementations21 Aug 2020 Dingheng Wang, Bijiao Wu, Guangshe Zhao, Man Yao, Hengnu Chen, Lei Deng, Tianyi Yan, Guoqi Li

Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition.

Tensor Decomposition Video Recognition

Hybrid Tensor Decomposition in Neural Network Compression

no code implementations29 Jun 2020 Bijiao Wu, Dingheng Wang, Guangshe Zhao, Lei Deng, Guoqi Li

We further theoretically and experimentally discover that the HT format has better performance on compressing weight matrices, while the TT format is more suited for compressing convolutional kernels.

Neural Network Compression Tensor Decomposition

Compressing 3DCNNs Based on Tensor Train Decomposition

no code implementations8 Dec 2019 Dingheng Wang, Guangshe Zhao, Guoqi Li, Lei Deng, Yang Wu

However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger than that of traditional two dimensional convolutional neural networks (2DCNNs).

Hand Gesture Recognition Hand-Gesture Recognition +3

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