pith. sign in

Integrity report for Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.20347 · pith:2026:YRTVWUDNFF775BDCOT64FXSC7K

0Critical
0Advisory
5Detectors run
2026-05-22Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-22 08:31:54.219800+00:00
doi_compliance completed v1.0.0 · findings 0 · 2026-05-22 08:31:37.319152+00:00
claim_evidence completed v1.0.0 · findings 0 · 2026-05-22 05:22:42.197287+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-21 19:54:16.124972+00:00
ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-05-21 03:33:30.075001+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/YRTVWUDN/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.