Knowledge Commons of Institute of Automation,CAS
Spiking Generative Adversarial Network with Attention Scoring Decoding | |
Feng, Linghao1,3; Zhao, Dongcheng1![]() ![]() | |
发表期刊 | Neural Networks
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2024 | |
页码 | 106423 |
摘要 | Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural networks, offer a closer approximation to brain-like processing due to their rich spatiotemporal dynamics. However, generative models based on spiking neural networks are not well studied. Particularly, previous works on generative adversarial networks based on spiking neural networks are conducted on simple datasets and do not perform well. In this work, we pioneer constructing a spiking generative adversarial network capable of handling complex images and having higher performance. Our first task is to identify the problems of out-of-domain inconsistency and temporal inconsistency inherent in spiking generative adversarial networks. We addressed these issues by incorporating the Earth-Mover distance and an attention-based weighted decoding method, significantly enhancing the performance of our algorithm across several datasets. Experimental results reveal that our approach outperforms existing methods on the MNIST, FashionMNIST, CIFAR10, and CelebA. In addition to our examination of static datasets, this study marks our inaugural investigation into event-based data, through which we achieved noteworthy results. Moreover, compared with hybrid spiking generative adversarial networks, where the discriminator is an artificial analog neural network, our methodology demonstrates closer alignment with the information processing patterns observed in the mouse. Our code can be found at https://github.com/Brain-Cog-Lab/sgad. |
学科门类 | 工学::控制科学与工程 |
DOI | https://doi.org/10.1016/j.neunet.2024.106423 |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
七大方向——子方向分类 | 类脑模型与计算 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57259 |
专题 | 脑图谱与类脑智能实验室_类脑认知计算 |
作者单位 | 1.Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences 2.Center for Excellence in Brain Science and Intelligence Technology, CAS, 3.School of Future Technology, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Feng, Linghao,Zhao, Dongcheng,Zeng, Yi. Spiking Generative Adversarial Network with Attention Scoring Decoding[J]. Neural Networks,2024:106423. |
APA | Feng, Linghao,Zhao, Dongcheng,&Zeng, Yi.(2024).Spiking Generative Adversarial Network with Attention Scoring Decoding.Neural Networks,106423. |
MLA | Feng, Linghao,et al."Spiking Generative Adversarial Network with Attention Scoring Decoding".Neural Networks (2024):106423. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
NN_SGAD.pdf(1067KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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