  This paper proposes a hypothesis for the aesthetic appreciation that
aesthetic images make a neural network strengthen salient concepts and discard
inessential concepts. In order to verify this hypothesis, we use multi-variate
interactions to represent salient concepts and inessential concepts contained
in images. Furthermore, we design a set of operations to revise images towards
more beautiful ones. In experiments, we find that the revised images are more
aesthetic than the original ones to some extent.
