How to train your ViT for OOD Detection

VisionTransformers have been shown to be powerful out-of-distribution detectors for ImageNet-scale settings when finetuned from publicly available checkpoints, often outperforming other model types on popular benchmarks. In this work, we investigate the impact of both the pretraining and finetuning scheme on the performance of ViTs on this task by analyzing a large pool of models. We find that the exact type of pretraining has a strong impact on which method works well and on OOD detection performance in general. We further show that certain training schemes might only be effective for a specific type of out-distribution, but not in general, and identify a best-practice training recipe. Deep neural networks have undeniably achieved remarkable success across a spectrum of realworld applications, showcasing outstanding performance. Nevertheless, they often exhibit unforeseen behaviour when confronted with unknown situations like receiving an input that is not related to the task it has been trained on.