  Despite the remarkable success of deep learning in pattern recognition, deep
network models face the problem of training a large number of parameters. In
this paper, we propose and evaluate a novel multi-path wavelet neural network
architecture for image classification with far less number of trainable
parameters. The model architecture consists of a multi-path layout with several
levels of wavelet decompositions performed in parallel followed by fully
connected layers. These decomposition operations comprise wavelet neurons with
learnable parameters, which are updated during the training phase using the
back-propagation algorithm. We evaluate the performance of the introduced
network using common image datasets without data augmentation except for SVHN
and compare the results with influential deep learning models. Our findings
support the possibility of reducing the number of parameters significantly in
deep neural networks without compromising its accuracy.
