Large Class Separation is not what you need for Relational
  Reasoning-based OOD Detection
Lorenzo Li Lu Giulia D'Ascenzi Francesco Cappio Borlino Tatiana Tommasi
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.