  Refraining from confidently predicting when faced with categories of inputs
different from those seen during training is an important requirement for the
safe deployment of deep learning systems. While simple to state, this has been
a particularly challenging problem in deep learning, where models often end up
making overconfident predictions in such situations. In this work we present a
simple, but highly effective approach to deal with out-of-distribution
detection that uses the principle of abstention: when encountering a sample
from an unseen class, the desired behavior is to abstain from predicting. Our
approach uses a network with an extra abstention class and is trained on a
dataset that is augmented with an uncurated set that consists of a large number
of out-of-distribution (OoD) samples that are assigned the label of the
abstention class; the model is then trained to learn an effective discriminator
between in and out-of-distribution samples. We compare this relatively simple
approach against a wide variety of more complex methods that have been proposed
both for out-of-distribution detection as well as uncertainty modeling in deep
learning, and empirically demonstrate its effectiveness on a wide variety of of
benchmarks and deep architectures for image recognition and text
classification, often outperforming existing approaches by significant margins.
Given the simplicity and effectiveness of this method, we propose that this
approach be used as a new additional baseline for future work in this domain.
