  Aesthetic assessment is subjective, and the distribution of the aesthetic
levels is imbalanced. In order to realize the auto-assessment of photo
aesthetics, we focus on using repetitive self-revised learning (RSRL) to train
the CNN-based aesthetics classification network by imbalanced data set. As
RSRL, the network is trained repetitively by dropping out the low likelihood
photo samples at the middle levels of aesthetics from the training data set
based on the previously trained network. Further, the retained two networks are
used in extracting highlight regions of the photos related with the aesthetic
assessment. Experimental results show that the CNN-based repetitive
self-revised learning is effective for improving the performances of the
imbalanced classification.
