  Image aesthetics assessment has been challenging due to its subjective
nature. Inspired by the scientific advances in the human visual perception and
neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task.
BDN first learns attributes through the parallel supervised pathways, on a
variety of selected feature dimensions. A high-level synthesis network is
trained to associate and transform those attributes into the overall aesthetics
rating. We then extend BDN to predicting the distribution of human ratings,
since aesthetics ratings are often subjective. Another highlight is our
first-of-its-kind study of label-preserving transformations in the context of
aesthetics assessment, which leads to an effective data augmentation approach.
Experimental results on the AVA dataset show that our biological inspired and
task-specific BDN model gains significantly performance improvement, compared
to other state-of-the-art models with the same or higher parameter capacity.
