extracting various low level hand-crafted image features to
train different types of classiﬁers [29], [30], usually using
datasets annotated with subjective aesthetic ranking scores
obtained through different experimental surveys [31]. In more
recent works, the adoption of learned deep features showed
signiﬁcant improvement [32], particularly when ﬁne-tuning
pre-trained CNN models for the task of predicting aesthetic
scores [33]–[35]. For a more detailed overview of studies in
computational aesthetics, we refer the reader to [36], [37].
A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art