  Deep neural networks are increasingly used in a wide range of technologies
and services, but remain highly susceptible to out-of-distribution (OOD)
samples, that is, drawn from a different distribution than the original
training set. A common approach to address this issue is to endow deep neural
networks with the ability to detect OOD samples. Several benchmarks have been
proposed to design and validate OOD detection techniques. However, many of them
are based on far-OOD samples drawn from very different distributions, and thus
lack the complexity needed to capture the nuances of real-world scenarios. In
this work, we introduce a comprehensive benchmark for OOD detection, based on
ImageNet and Places365, that assigns individual classes as in-distribution or
out-of-distribution depending on the semantic similarity with the training set.
Several techniques can be used to determine which classes should be considered
in-distribution, yielding benchmarks with varying properties. Experimental
results on different OOD detection techniques show how their measured efficacy
depends on the selected benchmark and how confidence-based techniques may
outperform classifier-based ones on near-OOD samples.
