  Deep learning models have been successfully used in computer vision and many
other fields. We propose an unorthodox algorithm for performing quantization of
the model parameters. In contrast with popular quantization schemes based on
thresholds, we use a novel technique based on periodic functions, such as
continuous trigonometric sine or cosine as well as non-continuous hat
functions. We apply these functions component-wise and add the sum over the
model parameters as a regularizer to the model loss during training. The
frequency and amplitude hyper-parameters of these functions can be adjusted
during training. The regularization pushes the weights into discrete points
that can be encoded as integers. We show that using this technique the
resulting quantized models exhibit the same accuracy as the original ones on
CIFAR-10 and ImageNet datasets.
