  We present Hinted Networks: a collection of architectural transformations for
improving the accuracies of neural network models for regression tasks, through
the injection of a prior for the output prediction (i.e. a hint). We ground our
investigations within the camera relocalization domain, and propose two
variants, namely the Hinted Embedding and Hinted Residual networks, both
applied to the PoseNet base model for regressing camera pose from an image. Our
evaluations show practical improvements in localization accuracy for standard
outdoor and indoor localization datasets, without using additional information.
We further assess the range of accuracy gains within an aerial-view
localization setup, simulated across vast areas at different times of the year.
