  Convolutional neural networks (CNNs) learn filters in order to capture local
correlation patterns in feature space. We propose to learn these filters as
combinations of preset spectral filters defined by the Discrete Cosine
Transform (DCT). Our proposed DCT-based harmonic blocks replace conventional
convolutional layers to produce partially or fully harmonic versions of new or
existing CNN architectures. Using DCT energy compaction properties, we
demonstrate how the harmonic networks can be efficiently compressed by
truncating high-frequency information in harmonic blocks thanks to the
redundancies in the spectral domain. We report extensive experimental
validation demonstrating benefits of the introduction of harmonic blocks into
state-of-the-art CNN models in image classification, object detection and
semantic segmentation applications.
