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How does the machine classify styles in art? And how does it relate to arthistorians' methods for analyzing style? Several studies have shown the abilityof the machine to learn and predict style categories, such as Renaissance,Baroque, Impressionism, etc., from images of paintings. This implies that themachine can learn an internal representation encoding discriminative featuresthrough its visual analysis. However, such a representation is not necessarilyinterpretable. We conducted a comprehensive study of several of thestate-of-the-art convolutional neural networks applied to the task of styleclassification on 77K images of paintings, and analyzed the learnedrepresentation through correlation analysis with concepts derived from arthistory. Surprisingly, the networks could place the works of art in a smoothtemporal arrangement mainly based on learning style labels, without any apriori knowledge of time of creation, the historical time and context ofstyles, or relations between styles. The learned representations showed thatthere are few underlying factors that explain the visual variations of style inart. Some of these factors were found to correlate with style patternssuggested by Heinrich W\"olfflin (1846-1945). The learned representations alsoconsistently highlighted certain artists as the extreme distinctiverepresentative of their styles, which quantitatively confirms art historianobservations.

The Shape of Art History in the Eyes of the Machine