  Deep Learning models possess two key traits that, in combination, make their
use in the real world a risky prospect. One, they do not typically generalize
well outside of the distribution for which they were trained, and two, they
tend to exhibit confident behavior regardless of whether or not they are
producing meaningful outputs. While Deep Learning possesses immense power to
solve realistic, high-dimensional problems, these traits in concert make it
difficult to have confidence in their real-world applications. To overcome this
difficulty, the task of Out-Of-Distribution (OOD) Detection has been defined,
to determine when a model has received an input from outside of the
distribution for which it is trained to operate.
  This paper introduces and examines a novel methodology, DOODLER, for OOD
Detection, which directly leverages the traits which result in its necessity.
By training a Variational Auto-Encoder (VAE) on the same data as another Deep
Learning model, the VAE learns to accurately reconstruct In-Distribution (ID)
inputs, but not to reconstruct OOD inputs, meaning that its failure state can
be used to perform OOD Detection. Unlike other work in the area, DOODLER
requires only very weak assumptions about the existence of an OOD dataset,
allowing for more realistic application. DOODLER also enables pixel-wise
segmentations of input images by OOD likelihood, and experimental results show
that it matches or outperforms methodologies that operate under the same
constraints.
