Optimizing deeper spiking neural networks for dynamic vision sensing
Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …
[PDF][PDF] Event-based Action Recognition Using Motion Information and Spiking Neural Networks.
Event-based cameras have attracted increasing attention due to their advantages of
biologically inspired paradigm and low power consumption. Since event-based cameras …
biologically inspired paradigm and low power consumption. Since event-based cameras …
Multi-level firing with spiking ds-resnet: Enabling better and deeper directly-trained spiking neural networks
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous
discrete and sparse characteristics, which have increasingly manifested their superiority in …
discrete and sparse characteristics, which have increasingly manifested their superiority in …
Effective AER object classification using segmented probability-maximization learning in spiking neural networks
Address event representation (AER) cameras have recently attracted more attention due to
the advantages of high temporal resolution and low power consumption, compared with …
the advantages of high temporal resolution and low power consumption, compared with …
Hardvs: Revisiting human activity recognition with dynamic vision sensors
The main streams of human activity recognition (HAR) algorithms are developed based on
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …
SSTFormer: bridging spiking neural network and memory support transformer for frame-event based recognition
Event camera-based pattern recognition is a newly arising research topic in recent years.
Current researchers usually transform the event streams into images, graphs, or voxels, and …
Current researchers usually transform the event streams into images, graphs, or voxels, and …
Low latency and sparse computing spiking neural networks with self-driven adaptive threshold plasticity
Spiking neural networks (SNNs) have captivated the attention worldwide owing to their
compelling advantages in low power consumption, high biological plausibility, and strong …
compelling advantages in low power consumption, high biological plausibility, and strong …
Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation
Variational autoencoders (VAEs) are influential generative models with rich representation
capabilities from the deep neural network architecture and Bayesian method. However, VAE …
capabilities from the deep neural network architecture and Bayesian method. However, VAE …
Mapping very large scale spiking neuron network to neuromorphic hardware
Neuromorphic hardware is a multi-core computer system specifically designed to run
Spiking Neuron Network (SNN) applications. As the scale of neuromorphic hardware …
Spiking Neuron Network (SNN) applications. As the scale of neuromorphic hardware …
Spike timing-based unsupervised learning of orientation, disparity, and motion representations in a spiking neural network
T Barbier, C Teulière, J Triesch - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Neuromorphic vision sensors present unique advantages over their frame based
counterparts. However, unsupervised learning of efficient visual representations from their …
counterparts. However, unsupervised learning of efficient visual representations from their …