  Out-of-distribution (OOD) detection can be used in deep learning-based
applications to reject outlier samples from being unreliably classified by deep
neural networks. Learning to classify between OOD and in-distribution samples
is difficult because data comprising the former is extremely diverse. It has
been observed that an auxiliary OOD dataset is most effective in training a
"rejection" network when its samples are semantically similar to
in-distribution images. We first deduce that OOD images are perceived by a deep
neural network to be semantically similar to in-distribution samples when they
share a common background, as deep networks are observed to incorrectly
classify such images with high confidence. We then propose a simple yet
effective Key In-distribution feature Replacement BY inpainting (KIRBY)
procedure that constructs a surrogate OOD dataset by replacing
class-discriminative features of in-distribution samples with marginal
background features. The procedure can be implemented using off-the-shelf
vision algorithms, where each step within the algorithm is shown to make the
surrogate data increasingly similar to in-distribution data. Design choices in
each step are studied extensively, and an exhaustive comparison with
state-of-the-art algorithms demonstrates KIRBY's competitiveness on various
benchmarks.
