  Automatic photo aesthetic assessment is a challenging artificial intelligence
task. Existing computational approaches have focused on modeling a single
aesthetic score or a class (good or bad), however these do not provide any
details on why the photograph is good or bad, or which attributes contribute to
the quality of the photograph. To obtain both accuracy and human interpretation
of the score, we advocate learning the aesthetic attributes along with the
prediction of the overall score. For this purpose, we propose a novel multitask
deep convolution neural network, which jointly learns eight aesthetic
attributes along with the overall aesthetic score. We report near human
performance in the prediction of the overall aesthetic score. To understand the
internal representation of these attributes in the learned model, we also
develop the visualization technique using back propagation of gradients. These
visualizations highlight the important image regions for the corresponding
attributes, thus providing insights about model's representation of these
attributes. We showcase the diversity and complexity associated with different
attributes through a qualitative analysis of the activation maps.
