Optimization of Artificial Neural Networks using Wavelet Transforms

Abstract

The article presents the artificial neural networks performance optimization using wavelet trans- form. The existing approaches of wavelet transform implementation in neural networks imply either transfor- mation before neural network or using “wavenet” architecture, which requires new neural network training approaches. The proposed approach is based on the representation of the neuron as a nonrecursive adaptive filter and wavelet filter application to obtain the low-frequency part of the image. It reduces the image size and filtering interference, which is usually high-frequency. Our wavelet transform model is based on the clas- sical representation of a forward propagation neural network or convolutional layers. It allows designing neu- ral networks with the wavelet transform based on existing libraries and does not require changes in the neural network training algorithm. It was tested on three MNIST-like datasets. As a result of testing, it was found that the speed gain is approximately 50 ± 5% with a slight loss of recognition quality of no more than 4%. For practitioner programmers, the proposed algorithm was tested on real images to distinguish animals and showed similar results as the MNIST-like tests.