V. RESULTS
The experimental
results are presented from several
viewpoints. First we analyze the attention maps of different
task-speciﬁc CNN models, as well as compare and evalu-
ate predicted aesthetic, sentiment and memorability scores.
Furthermore, we explore the relation of different image
attributes with the predicted aesthetic, sentiment and mem-
orability scores. Finally, we position the results in the context

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E. Cetinic et al.: Deep Learning Perspective on Beauty, Sentiment and Remembrance of Art

FIGURE 2. Examples of attention maps employed for predicting the aesthetic score (AestNet_3), sentiment score (SentiNet_3) and memorability
score (MemNet_3).

of art history and investigate their relation to style, genre,
artists, as well as how they change over time.
A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art