5 citations · 16 across the 8 of their papers we have counts for
6 papers · 1 filter
Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking
Xin Tong, Shi Peng, Baojie Tian +3
Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confi…
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
Yufei Guo, Yuanpei Chen, Xiaode Liu +4
The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to…
Membrane Potential Batch Normalization for Spiking Neural Networks
Yufei Guo, Yuhan Zhang, Yuanpei Chen +5
As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking neural networks (SNNs) have gained more and more interest recently. To train the deep mo…
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks
Yufei Guo, Xiaode Liu, Yuanpei Chen +5
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the r…
Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization
Yufei Guo, Weihang Peng, Yuanpei Chen +4
Emerged as a biology-inspired method, Spiking Neural Networks (SNNs) mimic the spiking nature of brain neurons and have received lots of research attention. SNNs deal with binary s…
Direct Learning-Based Deep Spiking Neural Networks: A Review
Yufei Guo, Xuhui Huang, Zhe Ma
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and even…