基于图像特征的卷积核初始化方法

针对当前卷积核初始化方法易导致网络不稳定及主成分分析算法对网络结构限制的问题, 提出一种基于图像特征的卷积核初始化方法. 该方法先结合模糊处理技术和边缘处理技术对图像进行采样, 再将采样后的数据随机分组, 使用主成分分析算法提取各组数据的主成分, 初始化卷积核. 将该方法应用于数据集Cifar-10和Corel-1000, 并与Gauss初始化方法和He初始化方法进行对比测试, 实验结果表明, 该方法性能优于其他卷积核初始化方法.

Convolution Kernel Initialization Method Based on Image Features

Aiming at the problem that the  current convolution kernel initialization method was easy to lead to network instability and the limitation of principal component analysis algorithm on network structure, we proposed a convolution kernel initialization method based on image features. Firstly, the method combined fuzzy processing technology and edge processing technology to sample images, and then the sampled data were randomly divided into groups. Principal component analysis algorithm was used to extract the principal components of each group of data, and the convolution kernel was initialized. We applied the method to Cifar-10 and Corel-1000 datasets, and compared it with Gaussian initialization method and He initialization method. The experimental results show that the performance of the method is superior to other convolution kernel initialization methods.