  Deep neural networks have found widespread adoption in solving complex tasks
ranging from image recognition to natural language processing. However, these
networks make confident mispredictions when presented with data that does not
belong to the training distribution, i.e. out-of-distribution (OoD) samples. In
this paper we explore whether the property of Vicinal Risk Minimization (VRM)
to smoothly interpolate between different class boundaries helps to train
better OoD detectors. We apply VRM to existing OoD detection techniques and
show their improved performance. We observe that existing OoD detectors have
significant memory and compute overhead, hence we leverage VRM to develop an
OoD detector with minimal overheard. Our detection method introduces an
auxiliary class for classifying OoD samples. We utilize mixup in two ways to
implement Vicinal Risk Minimization. First, we perform mixup within the same
class and second, we perform mixup with Gaussian noise when training the
auxiliary class. Our method achieves near competitive performance with
significantly less compute and memory overhead when compared to existing OoD
detection techniques. This facilitates the deployment of OoD detection on edge
devices and expands our understanding of Vicinal Risk Minimization for use in
training OoD detectors.
