Title: Extension of Convolutional Neural Network with General Image Processing Kernels,
Abstract: We applied pre-defined kernels also known as filters or masks developed for
image processing to convolution neural network. Instead of letting neural
networks find its own kernels, we used 41 different general-purpose kernels of
blurring, edge detecting, sharpening, discrete cosine transformation, etc. for
the first layer of the convolution neural networks. This architecture, thus
named as general filter convolutional neural network (GFNN), can reduce
training time by 30% with a better accuracy compared to the regular
convolutional neural network (CNN). GFNN also can be trained to achieve 90%
accuracy with only 500 samples. Furthermore, even though these kernels are not
specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble
nor any other special algorithms.