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Audio Classification 벤치마크

Audio Classification on SHD

22개 결과 · ⬇ CSV · JSON

Percentage correct

70.32 76.72 83.11 89.5 95.9 2019-10 2026-11 CNN — 92.4 (2019-10-16) Recurrent SNN — 83.2 (2019-10-16) CNN — 92.4 (2019-10-16) Recurrent SNN — 83.2 (2019-10-16) Sparse Spiking Gradient Descent — 77.5 (2021-05-18) Sparse Spiking Gradient Descent — 77.5 (2021-05-18) SNN with temporal-wise attention — 91.1 (2021-07-25) SNN with temporal-wise attention — 91.1 (2021-07-25) SNN — 87.0 (2022-04-21) SNN — 87.0 (2022-04-21) FastSNN — 70.32 (2022-05-30) FastSNN — 70.32 (2022-05-30) Recurrent convolutional SNN — 83.5 (2022-06-21) Recurrent convolutional SNN — 83.5 (2022-06-21) SNN with spatio-temporal filters and attention — 92.4 (2022-10-11) SNN with spatio-temporal filters and attention — 92.4 (2022-10-11) SNN featuring learnable axonal delays with adaptively delay caps — 92.45 (2023-02-16) SNN featuring learnable axonal delays with adaptively delay caps — 92.45 (2023-02-16) SNN with Dilated Convolution with Learnable Spacings — 95.1 (2023-06-30) SNN with Dilated Convolution with Learnable Spacings — 95.1 (2023-06-30) Event-SSM — 95.9 (2024-04-29) Event-SSM — 95.9 (2024-04-29) CNN — 92.4 (2019-10-16) SNN featuring learnable axonal delays with adaptively delay caps — 92.45 (2023-02-16) SNN with Dilated Convolution with Learnable Spacings — 95.1 (2023-06-30) Event-SSM — 95.9 (2024-04-29)
RankModel Percentage correct PaperCodeYear
1 Event-SSM 95.9 Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models Efficient-Scalable-Machine-Learning/event-ssm 2024
2 SNN with Dilated Convolution with Learnable Spacings 95.1 Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings thvnvtos/snn-delays 2023
3 SNN featuring learnable axonal delays with adaptively delay caps 92.45 Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition 2023
4 CNN 92.4 The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks 2019
4 SNN with spatio-temporal filters and attention 92.4 STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks Tab-ct/STSC-SNN 2022
6 SNN with temporal-wise attention 91.1 Temporal-wise Attention Spiking Neural Networks for Event Streams Classification 2021
7 SNN 87.0 MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks Tab-ct/MAP-SNN 2022
8 Recurrent convolutional SNN 83.5 Fluctuation-driven initialization for spiking neural network training fmi-basel/stork 2022
9 Recurrent SNN 83.2 The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks 2019
10 Sparse Spiking Gradient Descent 77.5 Sparse Spiking Gradient Descent npvoid/SparseSpikingBackprop 2021
11 FastSNN 70.32 Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks webstorms/fastsnn 2022
12 Event-SSM 95.9 Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models Efficient-Scalable-Machine-Learning/event-ssm 2024
13 SNN with Dilated Convolution with Learnable Spacings 95.1 Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings thvnvtos/snn-delays 2023
14 SNN featuring learnable axonal delays with adaptively delay caps 92.45 Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition 2023
15 CNN 92.4 The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks 2019
15 SNN with spatio-temporal filters and attention 92.4 STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks Tab-ct/STSC-SNN 2022
17 SNN with temporal-wise attention 91.1 Temporal-wise Attention Spiking Neural Networks for Event Streams Classification 2021
18 SNN 87.0 MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks Tab-ct/MAP-SNN 2022
19 Recurrent convolutional SNN 83.5 Fluctuation-driven initialization for spiking neural network training fmi-basel/stork 2022
20 Recurrent SNN 83.2 The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks 2019
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