  Even though convolutional neural networks have become the method of choice in
many fields of computer vision, they still lack interpretability and are
usually designed manually in a cumbersome trial-and-error process. This paper
aims at overcoming those limitations by proposing a deep neural network, which
is designed in a systematic fashion and is interpretable, by integrating
multiresolution analysis at the core of the deep neural network design. By
using the lifting scheme, it is possible to generate a wavelet representation
and design a network capable of learning wavelet coefficients in an end-to-end
form. Compared to state-of-the-art architectures, the proposed model requires
less hyper-parameter tuning and achieves competitive accuracy in image
classification tasks
