  Standard recognition approaches are unable to deal with novel categories at
test time. Their overconfidence on the known classes makes the predictions
unreliable for safety-critical applications such as healthcare or autonomous
driving. Out-Of-Distribution (OOD) detection methods provide a solution by
identifying semantic novelty. Most of these methods leverage a learning stage
on the known data, which means training (or fine-tuning) a model to capture the
concept of normality. This process is clearly sensitive to the amount of
available samples and might be computationally expensive for on-board systems.
A viable alternative is that of evaluating similarities in the embedding space
produced by large pre-trained models without any further learning effort. We
focus exactly on such a fine-tuning-free OOD detection setting. This works
presents an in-depth analysis of the recently introduced relational reasoning
pre-training and investigates the properties of the learned embedding,
highlighting the existence of a correlation between the inter-class feature
distance and the OOD detection accuracy. As the class separation depends on the
chosen pre-training objective, we propose an alternative loss function to
control the inter-class margin, and we show its advantage with thorough
experiments.
