  Near out-of-distribution detection (OOD) is a major challenge for deep neural
networks. We demonstrate that large-scale pre-trained transformers can
significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks
across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD
detection, we improve the AUROC from 85% (current SOTA) to more than 96% using
Vision Transformers pre-trained on ImageNet-21k. On a challenging genomics OOD
detection benchmark, we improve the AUROC from 66% to 77% using transformers
and unsupervised pre-training. To further improve performance, we explore the
few-shot outlier exposure setting where a few examples from outlier classes may
be available; we show that pre-trained transformers are particularly
well-suited for outlier exposure, and that the AUROC of OOD detection on
CIFAR-100 vs CIFAR-10 can be improved to 98.7% with just 1 image per OOD class,
and 99.46% with 10 images per OOD class. For multi-modal image-text pre-trained
transformers such as CLIP, we explore a new way of using just the names of
outlier classes as a sole source of information without any accompanying
images, and show that this outperforms previous SOTA on standard vision OOD
benchmark tasks.
