  We evaluate the out-of-distribution (OOD) detection performance of
self-supervised learning (SSL) techniques with a new evaluation framework.
Unlike the previous evaluation methods, the proposed framework adjusts the
distance of OOD samples from the in-distribution samples. We evaluate an
extensive combination of OOD detection algorithms on three different
implementations of the proposed framework using simulated samples, images, and
text. SSL methods consistently demonstrated the improved OOD detection
performance in all evaluation settings.
