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Found 15 papers, 6 papers with code
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology.
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention.
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking
To the best of our knowledge, DashNet is the first framework that can integrate and process ANNs and SNNs in a hybrid paradigm, which provides a novel solution to achieve both effectiveness and efficiency for high-speed object tracking.
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency.
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps.
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion.
Ranked #11 on
Gesture Recognition
on DVS128 Gesture
Brain-inspired global-local learning incorporated with neuromorphic computing
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
Going Deeper With Directly-Trained Larger Spiking Neural Networks
To this end, we propose a threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed "STBP-tdBN", enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware.
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments.
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits their application in many practical tasks.
Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks
In this paper, we identify the crux and then propose a novel residual block for SNNs, which is able to significantly extend the depth of directly trained SNNs, e. g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem.
Advancing Spiking Neural Networks towards Deep Residual Learning
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice.
Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation
This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model.
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut.
Multi-source domain adaptation for regression
First, we extend a flexible single-source DA algorithm for classification through outcome-coarsening to enable its application to regression problems.
