AWhat changed
Uses negative log-sum-exp of classifier logits as a non-probabilistic anomaly score and proposes margin-based fine-tuning to separate in-distribution and auxiliary-outlier energies.
Scientific scope
Uses negative log-sum-exp of classifier logits as a non-probabilistic anomaly score and proposes margin-based fine-tuning to separate in-distribution and auxiliary-outlier energies. Energy-model algebra does not establish that an arbitrary discriminative network learns the true normalized input density or detects all OOD inputs. Fine-tuning requires auxiliary outliers; source benchmark gains do not certify deployment safety. General computing contribution only.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Energy-model algebra does not establish that an arbitrary discriminative network learns the true normalized input density or detects all OOD inputs. Fine-tuning requires auxiliary outliers; source benchmark gains do not certify deployment safety. General computing contribution only.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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