Deep Scattering Network with Max-Pooling

Taekyung Ki, Youngmi Hur

Abstract: Scattering network is a convolutional network, consisting of cascading convolutions using pre-defined wavelets followed by the modulus operator [1]. The scattering network is one of few mathematical tools explaining the convolutional neural networks (CNNs). However, a pooling operator, which is a main component of CNNs, is not considered in the original scattering network. We model a continuous max-pooling, apply it to the scattering network, and get a new network named scattering-maxp network. We show that the scattering-maxp network shares many useful properties of the scattering network including translation invariance, and we conduct numerical experiments showing the computational advantage of our network. We summarize our main findings below. More details about our results can be found in [2] and Python codes are available in https://github.com/TaekvungKi/Scattering_maxp.