Current Anomaly Detectors are Anomalous: On Semantic Treatment of OOD Inputs

Ramneet Kaur, Xiayan Ji, Souradeep Dutta, Yahan Yang, Michele Caprio, Elena Bernardis, Oleg Sokolsky, Insup Lee

Keywords: machine learning, training distribution, out-of-distribution, OODs, detection, semantic information

TL;DR: We propose that in-distribution should not be tied to the training distribution but to the distribution of semantic information in training data, and therefore OOD detection should be performed on the semantic information extracted from training data

Abstract: Machine learning models have achieved impressive performance across different modalities. It is well known that these models are prone to making mistakes on out-of-distribution inputs. OOD detection has, therefore, gained a lot of attention recently. We observe that most existing detectors use the distribution estimated by the training dataset for OOD detection. This can be a serious impediment since faulty OOD detectors can potentially restrict utility of the model. Such detectors, tied to the bias in data collection process, can be impermeable to inputs lying outside the training distribution but with the same semantic information (e.g., class labels) as the training data. We argue that in-distribution should not be tied to just the training distribution but to the distribution of the semantic information contained in the training data. To support our argument, we perform OOD detection on semantic information extracted from the training data of MNIST and COCO datasets, and show that it not only reduces false alarms but also significantly improves detection of OOD inputs with spurious features from training data.

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