使用CSNN在ASL-DVS上执行基于事件的数据处理与分类
Using CSNNs to Perform Event-based Data Processing & Classification on ASL-DVS
AI总结：
本文提出一种卷积脉冲神经网络CSNN，在ASL-DVS手势数据集上学习时空关系，实现24个美国手语字母的识别，训练准确率达100%。
摘要：
近年来，受生物启发的视觉感知和神经形态计算的进展推动了多种高效生物启发方案的发展，并已在现实世界中得到应用。一个值得关注的应用是将基于事件的相机与脉冲神经网络（SNNs）相结合，以处理异步且稀疏的基于事件的序列，这类序列通常难以处理。在本项目中，我们开发了一种卷积脉冲神经网络（CSNN）架构，利用卷积运算和脉冲神经元的循环特性来学习ASL-DVS手势数据集中的空间和时间关系。ASL-DVS手势数据集是一个神经形态数据集，包含美国手语（ASL）中24个字母（A到Y，不包括J和Z，因为其符号性质特殊）的手势。我们对完整ASL-DVS数据集的一个预处理子集进行了分类，以识别字母手势，并达到了100%的训练准确率。具体而言，这一结果是在Google Cloud计算平台上训练得到的，使用的学习率为0.0005，批量大小为25（共20个批次），迭代200次，训练10个轮次。
英文摘要：
Recent advancements in bio-inspired visual sensing and neuromorphic computing have led to the development of various highly efficient bio-inspired solutions with real-world applications. One notable application integrates event-based cameras with spiking neural networks (SNNs) to process event-based sequences that are asynchronous and sparse, making them difficult to handle. In this project, we develop a convolutional spiking neural network (CSNN) architecture that leverages convolutional operations and recurrent properties of a spiking neuron to learn the spatial and temporal relations in the ASL-DVS gesture dataset. The ASL-DVS gesture dataset is a neuromorphic dataset containing hand gestures when displaying 24 letters (A to Y, excluding J and Z due to the nature of their symbols) from the American Sign Language (ASL). We performed classification on a pre-processed subset of the full ASL-DVS dataset to identify letter signs and achieved 100\% training accuracy. Specifically, this was achieved by training in the Google Cloud compute platform while using a learning rate of 0.0005, batch size of 25 (total of 20 batches), 200 iterations, and 10 epochs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
