The relative feature importances obtained from all models
are shown in Fig. 8. Feature importances obtained from the
AestNet_3, SentiNet_3 and MemNet_3 models indicate that
the obtained results are robust with respect to the two most
inﬂuential features for all regression tasks and models used.
Speciﬁcally, features expressing the amount of lighting and
positively rated content are dominant in determining aesthet-
ics scores, while color vividness and harmony are crucial in
inﬂuencing visual sentiment scores. Object emphasis clearly

1Simpliﬁed tree-based classiﬁer and regressor for interpretable machine

learning, https://github.com/tmadl/sklearn-interpretable-tree

stands out as the most important feature for predicting mem-
orability scores.
A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art