  In this paper, we utilize weight gradients from backpropagation to
characterize the representation space learned by deep learning algorithms. We
demonstrate the utility of such gradients in applications including perceptual
image quality assessment and out-of-distribution classification. The
applications are chosen to validate the effectiveness of gradients as features
when the test image distribution is distorted from the train image
distribution. In both applications, the proposed gradient based features
outperform activation features. In image quality assessment, the proposed
approach is compared with other state of the art approaches and is generally
the top performing method on TID 2013 and MULTI-LIVE databases in terms of
accuracy, consistency, linearity, and monotonic behavior. Finally, we analyze
the effect of regularization on gradients using CURE-TSR dataset for
out-of-distribution classification.
