github链接:GitHub - BICLab/Attention-SNN: Offical implementation of "Attention Spiking Neural Networks" (IEEE T-PAMI2023) 导读 脉冲神经网络(SNN)与人工神经网络(ANN)之间的性能差距是影响SNN普及的重大障碍,许多现实世界的平台都有资源和电池的限制。为了充分发挥SNN的潜力,作者研究了注意力机制,提出在SNN中使...
CNN-based的SNN和带有Attention的SNN: TA是在时间维度上做attention,借鉴的思想来源于SE网络;CA和SA分别是指在channel和spatial上做attention,借鉴的思想源于CBAM网络。 下面说明了TA、CA、SA的位置选择,做了ablation experiments,最终选择是MA-SNN。 下面是具体加attention的位置以及使用的网络backbone,本文用的是Att-Re...
Change the values of T and dt in /MA_SNN/DVSGestures/CNN/Config.py then run the tasks in /MA_SNN/DVSGestures. eg: python Att_SNN_CNN.py View the results in /MA_SNN/DVSGestures/CNN/Result/. 2. CIFAR10-DVS Download CIFAR10-DVS and processing dataset using official matlab program, ...
其中λ和α是固定数值(分别为 1.0507 和 1.6726)。这些值背后的推论(零均值/单位方差)构成了自归一化神经网络的基础(SNN)。 11. SReLU S 型整流线性激活单元(S-shaped Rectified Linear Activation Unit,SReLU)属于以 ReLU 为代表的整流激活函数族。它由三个分段线性函数组成。其中两种函数的斜度,以及函数相交的...
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 in...
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks do not fully exploit this feature. Moreover, they often overlook the role of adaptive threshold in spiking neurons, wh...
The attention map is then used to modulate the encoding layer of the SNN so that it focuses on the most informative sensory input. To facilitate direct learning of attention maps and avoid labor-intensive annotations, we propose a general principle and a corresponding weakly-supervised objective, ...
The 0-th and 1-th dimension of snn layer's input and output are batch-dimension and time-dimension. The most straightforward way of training higher quality models is by increasing their size. In this work, we would like to see that deepening network structures could get rid of the degradat...
Moreover, compressed SNN models generated by our methods can have up to 12.2x better compute energy-efficiency compared to ANNs that have a similar number of parameters. 对芯片边缘智能的需求不断增长,这促使我们探索算法技术和专用硬件,以降低当前机器学习模型的计算能量。深度脉冲神经网络 (SNN) 特别...
这是一篇在SNN的时间维度上引入attention的文章,中了2021年的ICCV。 论文传送门:ICCV 2021 Open Access Repository Abstract 由于事件(events)通常是稀疏和非均匀的,并且具有µs的时间分辨率,所以如何有效和高效地处理时空事件流具有很大的价值。SNN具有从事件流中提取有效时空特征的潜力。将单个事件聚合为更高时间分辨...