  CNNs exhibit many behaviors different from humans, one of which is the
capability of employing high-frequency components. This paper discusses the
frequency bias phenomenon in image classification tasks: the high-frequency
components are actually much less exploited than the low- and mid-frequency
components. We first investigate the frequency bias phenomenon by presenting
two observations on feature discrimination and learning priority. Furthermore,
we hypothesize that (i) the spectral density, (ii) class consistency directly
affect the frequency bias. Specifically, our investigations verify that the
spectral density of datasets mainly affects the learning priority, while the
class consistency mainly affects the feature discrimination.
