  Do we really understand how machine classifies art styles? Historically, art
is perceived and interpreted by human eyes and there are always controversial
discussions over how people identify and understand art. Historians and general
public tend to interpret the subject matter of art through the context of
history and social factors. Style, however, is different from subject matter.
Given the fact that Style does not correspond to the existence of certain
objects in the painting and is mainly related to the form and can be correlated
with features at different levels.(Ahmed Elgammal et al. 2018), which makes the
identification and classification of the characteristics artwork's style and
the "transition" - how it flows and evolves - remains as a challenge for both
human and machine. In this work, a series of state-of-art neural networks and
manifold learning algorithms are explored to unveil this intriguing topic: How
does machine capture and interpret the flow of Art History?
