  The convolutional layers are core building blocks of neural network
architectures. In general, a convolutional filter applies to the entire
frequency spectrum of the input data. We explore artificially constraining the
frequency spectra of these filters and data, called band-limiting, during
training. The frequency domain constraints apply to both the feed-forward and
back-propagation steps. Experimentally, we observe that Convolutional Neural
Networks (CNNs) are resilient to this compression scheme and results suggest
that CNNs learn to leverage lower-frequency components. In particular, we
found: (1) band-limited training can effectively control the resource usage
(GPU and memory); (2) models trained with band-limited layers retain high
prediction accuracy; and (3) requires no modification to existing training
algorithms or neural network architectures to use unlike other compression
schemes.
