mlsanityData to 7 Oct 2026

Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy

arXiv 2401.12129 ↗First posted 22 Jan 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

As deep neural networks become adopted in high-stakes domains, it is crucial to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence -- ultimately to know when networks' decisions (and their uncertainty in those decisions) should be trusted. In this paper we introduce Ablated Learned Temperature Energy (or "AbeT" for short), an OOD detection method which lowers the False Positive Rate at 95% True Positive Rate (FPR@95) by 43.43% in classification compared to state of the art without training networks in multiple stages or requiring hyperparameters or test-time backward passes. We additionally provide empirical insights as to why our model learns to distinguish between In-Distribution (ID) and OOD samples while only being explicitly trained on ID samples via exposure to misclassified ID examples at training time. Lastly, we show the efficacy of our method in identifying predicted bounding boxes and pixels corresponding to OOD objects in object detection and semantic segmentation, respectively -- with an AUROC increase of 5.15% in object detection and both a decrease in FPR@95 of 41.48% and an increase in AUPRC of 34.20% in semantic segmentation compared to previous state of the art.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 48 of its 60 references that are in the map. First posted on arXiv 22 Jan 2024.

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48 of 60 references.

References · 48

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