Apart from classiﬁcation, the use of CNN-based fea-
tures showed promising results in other topics of interests
such as retrieving visual links in paintings collections [20],
recognizing objects in paintings [21], [22] or distinguish-
ing illustrations from photographs [23]. Recently there has
been an emerging tendency towards enhancing the inter-
pretability of learned representations, as well as understand-
ing their relation to domain-speciﬁc properties and their
position in a wider semantic context. Elgammal et al. [24]
performed a correlation analysis of learned features extracted
from several different style-trained CNN models in order
to understand how learned representations are related to art
history methodologies for identifying styles. Furthermore,
Brachmann et al. [25] used CNN features to understand the
speciﬁc visual properties of artworks in comparison to natural
images.
A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art