为了减少推理所需的时间步骤,我们扩展了混合SNN训练策略,支持基于SL的SNN训练。所提出的方法只需要全局目标参数密度,而不像ADMM,我们需要为模型的每一层提供超参数。 In summary, we provide the following contributions: We propose the first attention-guided non-iterative compression (AGC) technique for deep ...
The implementation of Att-VGG-SNN inhttps://github.com/ridgerchu/SNN_Attention_VGG /module/Attention.py defines the Attention layer and /module/LIF.py,LIF_Module.py defines LIF module. The CSA-MS-ResNet104 model is available athttps://pan.baidu.com/s/1Uro7IVSerV23OKbG8Qn6pQ?pwd=54tl...
To leverage the temporal potential of SNNs, we propose a self-attention-based temporal-channel joint attention SNN (STCA-SNN) with end-to-end training, which infers attention weights along both temporal and channel dimensions concurrently. It models global temporal and channel information correlations...
Sa-SNN: spiking attention neural network for image classification doi:10.7717/peerj-cs.2549PeerJ Computer ScienceDan, YongpingWang, ZhidaLi, HengyiWei, Jintong
TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks [TNNLS 2024] How to Run First clone the repository. git clone https://github.com/ridgerchu/TCJA cd TCJA pip install -r requirements.txt Train DVS128 Detailed usage of the script could be found in the source file. python...
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