In order to explore the transferability of learned aesthetic,
sentiment and memorability features from the domain of
natural images to the domain of ﬁne art images, we employ
several different CNN models that were trained on differ-
ent domain-speciﬁc natural image datasets. Based on the
availability and quality of existing models and datasets,
we identify different datasets for each task. For the purpose
of aesthetic quality prediction, we employ models that were
trained on three different datasets: AADB [34] , AVA [31]
and FLICKR-AES [52]. For visual sentiment classiﬁcation
we employ models that were trained on two different datasets:
Twitter DeepSent [41] and Flickr Sentiment [53]. For memo-
rability prediction, we use models trained on the LaMem [47]
and SUN Memorability [54] datasets.
A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art