  Despite the recent developments that allowed neural networks to achieve
impressive performance on a variety of applications, these models are
intrinsically affected by the problem of overgeneralization, due to their
partitioning of the full input space into the fixed set of target classes used
during training. Thus it is possible for novel inputs belonging to categories
unknown during training or even completely unrecognizable to humans to fool the
system into classifying them as one of the known classes, even with a high
degree of confidence. Solving this problem may help improve the security of
such systems in critical applications, and may further lead to applications in
the context of open set recognition and 1-class recognition. This paper
presents a novel way to compute a confidence score using denoising autoencoders
and shows that such confidence score can correctly identify the regions of the
input space close to the training distribution by approximately identifying its
local maxima.
