When OOD Classes Were Seen Upstream: Effects of Class-Specific Supervision on Downstream OOD Detection

Out-of-distribution (OOD) detection is typically evaluated with respect to a downstream task, but the representation used by the detector may have encountered an OOD class during upstream training. We ask whether the upstream supervision history of a class affects its detectability as OOD downstream. To estimate this effect, we introduce a controlled evaluation in which the downstream task, evaluation data, architecture, training objective, and number of upstream classes are held fixed, while future-OOD classes either receive supervised treatment upstream or are withheld from upstream training. We make this comparison through a grouped rotating leave-one-out construction across multiple semantic classes. Experiments on a WordNet-structured ImageNet subset, CIFAR-100, and iNaturalist 2021 show that, under cosine kNN, future-OOD classes supervised upstream are systematically easier to detect downstream than matched classes withheld from upstream training. The mean AUROC difference is 5.9–7.5 points across the three datasets, and the effect persists with from-scratch Vision Transformers, with differences of 2.6–4.9 points. Across nine established OOD detectors, upstream supervision consistently affects OOD performance, but the magnitude and direction vary by detector. These results show that the upstream provenance of evaluated OOD classes is a previously underexamined source of variation in OOD detection, highlighting the importance of accounting for class provenance in OOD evaluation.