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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.
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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.
The text was updated successfully, but these errors were encountered: