Spiking Neural Network as Adaptive Event Stream Slicer
Authors: Jiahang Cao, Mingyuan Sun, Ziqing Wang, Hao Cheng, Qiang Zhang, shibo zhou, Renjing Xu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method yields significant performance improvements in event-based object tracking and recognition. |
| Researcher Affiliation | Collaboration | Jiahang Cao1 Mingyuan Sun2 Ziqing Wang3 Hao Cheng1 Qiang Zhang1,4 Shibo Zhou5 Renjing Xu1 1 The Hong Kong University of Science and Technology (Guangzhou) 2 Northeastern University 3 Northwestern University 4 Beijing Innovation Center of Humanoid Robotics Co. Ltd. 5 Brain Mind Innovation |
| Pseudocode | Yes | Algorithm 1 Adaptive Event Slicing Process and Algorithm 2 Feedback-Update Training Strategy |
| Open Source Code | Yes | Our code is available at https://github.com/Andy Cao1125/Spike Slicer. |
| Open Datasets | Yes | Datasets. The FE108 dataset [3] is an extensive event-based dataset for single object tracking... The DVS-Gesture [34] dataset contains 11 hand gestures... The N-Caltech101 dataset [35] incorporates 8,831 event-based images... The DVS-CIFAR10 dataset [36] is an event-stream dataset... The SL-Animal database [37] features DVS recordings... |
| Dataset Splits | No | We choose 54 sequences for training ANNs, 22 sequences for training SNNs and the rest 32 sequences for testing. |
| Hardware Specification | Yes | Each experiment is conducted in an NVIDIA 4090 GPU. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., PyTorch, Python versions) are mentioned. |
| Experiment Setup | Yes | We adopt the SGD optimizer and set the initial learning rate as 1e-4, along with the cosine learning rate scheduler. SNN models are trained with batch size 32. |