We discuss issues of modular learning in artificial neural networks and explore possibilities
of the partial use of modules when the computational resources are limited. The proposed
method is based on the ability of a wavelet transform to separate information into high-and
low-frequency parts. Using the expertise gained in developing convolutional wavelet neural
networks, the authors perform a transverse-layer partitioning of the network into modules for
the further partial use on devices with low computational capability. The theoretical …