The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in early-stage prototyping scenarios for both network models and hardware platforms, for which it provides several advantages. First, it is smaller and therefore faster to learn, thereby being better suited for small-scale exploratory studies in both software simulations and hardware prototypes. Second, it exhibits a very clear gap between the accuracies achievable using shallow as compared to deep neural networks. Third, it is easily transferable between spatial and temporal input domains, making it interesting for different types of classification scenarios.
翻译:yin-Yang数据集的开发是为了研究生物上可信的错误反向反向转换和神经网络的深度学习,它是传统深层学习数据集的替代方法,特别是在网络模型和硬件平台的早期原型假设中,为这些模型和硬件平台提供了若干优势。首先,该数据集较小,因此学习速度更快,因此更适合软件模拟和硬件原型的小规模探索性研究。第二,该数据集展示了利用浅度与深层神经网络相比可以实现的优缺点之间的非常明显差距。第三,该数据集很容易在空间输入领域和时间输入领域之间转移,因此对不同类型的分类方案很有意思。