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Found 12 papers, 9 papers with code
Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks
Despite the efforts of some online training methods, tackling spatial credit assignments by alternatives with comparable performance as spatial BP remains a significant problem.
Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning
Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to precise spiking times and sparse spikes with event-driven computation.
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications.
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
In particular, our method achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
In this paper, we study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method, implicit differentiation on the equilibrium state (IDE), for supervised learning with purely spike-based computation, which demonstrates the potential for energy-efficient training of SNNs.
Online Training Through Time for Spiking Neural Networks
With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form.
Ranked #3 on
Event data classification
on CIFAR10-DVS
Invertible Rescaling Network and Its Extensions
To be specific, we develop invertible models to generate valid degraded images and meanwhile transform the distribution of lost contents to the fixed distribution of a latent variable during the forward degradation.
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
In this paper, we propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance that is competitive to ANNs yet with low latency.
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
In this work, we consider feedback spiking neural networks, which are more brain-like, and propose a novel training method that does not rely on the exact reverse of the forward computation.
Modeling Lost Information in Lossy Image Compression
Specifically, ILC introduces an invertible encoding module to replace the encoder-decoder structure to produce the low dimensional informative latent representation, meanwhile, transform the lost information into an auxiliary latent variable that won't be further coded or stored.
Invertible Image Rescaling
High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.
Ranked #1 on
Image Rescaling
on Urban100-4x
TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion
Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.
