  Semantic novelty detection aims at discovering unknown categories in the test
data. This task is particularly relevant in safety-critical applications, such
as autonomous driving or healthcare, where it is crucial to recognize unknown
objects at deployment time and issue a warning to the user accordingly. Despite
the impressive advancements of deep learning research, existing models still
need a finetuning stage on the known categories in order to recognize the
unknown ones. This could be prohibitive when privacy rules limit data access,
or in case of strict memory and computational constraints (e.g. edge
computing). We claim that a tailored representation learning strategy may be
the right solution for effective and efficient semantic novelty detection.
Besides extensively testing state-of-the-art approaches for this task, we
propose a novel representation learning paradigm based on relational reasoning.
It focuses on learning how to measure semantic similarity rather than
recognizing known categories. Our experiments show that this knowledge is
directly transferable to a wide range of scenarios, and it can be exploited as
a plug-and-play module to convert closed-set recognition models into reliable
open-set ones.
