  The ability to identify whether or not a test sample belongs to one of the
semantic classes in a classifier's training set is critical to practical
deployment of the model. This task is termed open-set recognition (OSR) and has
received significant attention in recent years. In this paper, we first
demonstrate that the ability of a classifier to make the 'none-of-above'
decision is highly correlated with its accuracy on the closed-set classes. We
find that this relationship holds across loss objectives and architectures, and
further demonstrate the trend both on the standard OSR benchmarks as well as on
a large-scale ImageNet evaluation. Second, we use this correlation to boost the
performance of a maximum logit score OSR 'baseline' by improving its closed-set
accuracy, and with this strong baseline achieve state-of-the-art on a number of
OSR benchmarks. Similarly, we boost the performance of the existing
state-of-the-art method by improving its closed-set accuracy, but the resulting
discrepancy with the strong baseline is marginal. Our third contribution is to
present the 'Semantic Shift Benchmark' (SSB), which better respects the task of
detecting semantic novelty, in contrast to other forms of distribution shift
also considered in related sub-fields, such as out-of-distribution detection.
On this new evaluation, we again demonstrate that there is negligible
difference between the strong baseline and the existing state-of-the-art.
Project Page: https://www.robots.ox.ac.uk/~vgg/research/osr/
