Event data classification
7 papers with code • 4 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Event data classification
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N-Caltech 101
N-CARS
CIFAR10-DVS
Most implemented papers
A predictive model for the identification of learning styles in MOOC environments
Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners.
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature.
Neuromorphic Data Augmentation for Training Spiking Neural Networks
intelligent-computing-lab-yale/nda_snn
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In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance.
A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks
Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons.
Ecsnet: Spatio-temporal feature learning for event camera
happychenpipi/ECSNet
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IEEE Transactions on Circuits and Systems for Video Technology 2022
To fully exploit their inherent sparsity with reconciling the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS).
Online Training Through Time for Spiking Neural Networks
pkuxmq/ottt-snn
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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.
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
event-ahu/agcn_event_classification
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To address these issues, we propose a novel dual point-voxel absorbing graph representation learning for event stream data representation.
