But to truly assess the utility of cross-validation, we should first include an additional benchmark setting where we evaluate OOD detection on a training dataset with some outliers included (during training not just during testing). Cross-validation may still help in this setting.
This example: https://github.com/cleanlab/examples/tree/master/outlier_detection_cifar10 could become much more straightforward without cross-validation now that we've seen it doesn't help too much in the train/test OOD settings.
But to truly assess the utility of cross-validation, we should first include an additional benchmark setting where we evaluate OOD detection on a training dataset with some outliers included (during training not just during testing). Cross-validation may still help in this setting.