What is recorded here?
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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Weitang Liu; Xiaoyun Wang; John D. Owens; Yixuan Li
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.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Energy-based OOD scoring and energy-bounded fine-tuning
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
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Weitang Liu; Xiaoyun Wang; John D. Owens; Yixuan Li
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.