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We applied pre-defined kernels also known as filters or masks developed forimage processing to convolution neural network. Instead of letting neuralnetworks find its own kernels, we used 41 different general-purpose kernels ofblurring, edge detecting, sharpening, discrete cosine transformation, etc. forthe first layer of the convolution neural networks. This architecture, thusnamed as general filter convolutional neural network (GFNN), can reducetraining time by 30% with a better accuracy compared to the regularconvolutional neural network (CNN). GFNN also can be trained to achieve 90%accuracy with only 500 samples. Furthermore, even though these kernels are notspecialized for the MNIST dataset, we achieved 99.56% accuracy without ensemblenor any other special algorithms.

Extension of Convolutional Neural Network with General Image Processing Kernels