Paper Citation Record · LEDGER

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data

As of 22 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.12952.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.12952 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:27:20.022776Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact1
verified fuzzy28
unresolved15
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4ba39bc2-2cc0-4683-96b5-ede86dc30d64 · outbound

This paper cites Latent space autore- gression for novelty detection.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Latent space autore- gression for novelty detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.609980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7dc145c2-9d0a-4a24-9da1-5ed0af216fbb · outbound

This paper cites How Does Unlabeled Data Provably Help Out-of-Distribution Detection?.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data How Does Unlabeled Data Provably Help Out-of-Distribution Detection?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.892958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65c45275-2594-45a0-9f5f-7f8a6126b795 · outbound

This paper cites On the learnability of out-of-distribution de- tection.Journal of Machine Learning Research, 25,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data On the learnability of out-of-distribution de- tection.Journal of Machine Learning Research, 25,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.542199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 01a2c8a5-1087-41bc-9a22-dc7c7c5d0c4e · outbound

This paper cites Leveraging Unlabeled Data to Track Memorization.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Leveraging Unlabeled Data to Track Memorization

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:27:20.218723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9c832b88-d17a-45ce-9f20-5ba35a1b10eb · outbound

This paper cites Who said what: Modeling indi- vidual labelers improves classification.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Who said what: Modeling indi- vidual labelers improves classification

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.529427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3a55f1d2-7201-4cdd-8a37-b4b35edd92ef · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.908037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80b2202b-a974-447a-b99f-858044fc2883 · outbound

This paper cites Training ood de- tectors in their natural habitats.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Training ood de- tectors in their natural habitats

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.517722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 72e171af-52d9-457c-bb00-83edc5ee218f · outbound

This paper cites Learning multiple lay- ers of features from tiny images.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Learning multiple lay- ers of features from tiny images

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.506244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 94e07c3e-5284-49da-988e-212f5d9f6e24 · outbound

This paper cites Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.923170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dad90616-7ddd-404f-8914-53294a4c5906 · outbound

This paper cites A simple unified framework for detect- ing out-of-distribution samples and adversarial attacks.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data A simple unified framework for detect- ing out-of-distribution samples and adversarial attacks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.494251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c9549a4-2a33-4c97-bd4e-fb0ef2186c83 · outbound

This paper cites Disc: Learning from noisy labels via dynamic instance-specific selection and correction.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Disc: Learning from noisy labels via dynamic instance-specific selection and correction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.481635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b634f056-a99c-4f86-b8fa-d33a2e2a9921 · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.934727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58639689-8084-49a9-9b01-b5d4f99c3018 · outbound

This paper cites Mitigating label noise through data ambigua- tion.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Mitigating label noise through data ambigua- tion

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.470710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 41dbebfe-93bd-42a0-a532-0db231f2a84e · outbound

This paper cites Learning the latent causal structure for modeling label noise.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Learning the latent causal structure for modeling label noise

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.459808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 41651771-4d40-46c6-92ad-814cb19000e0 · outbound

This paper cites Open set learning with counterfactual images.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Open set learning with counterfactual images

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.436827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ecfd6e2a-368c-4102-aba3-801740c7509d · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Reading digits in natural images with unsupervised feature learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.952407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a291e811-63c2-4f2a-8ec2-b144798ff65a · outbound

This paper cites Dice: Lever- aging sparsification for out-of-distribution detection.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Dice: Lever- aging sparsification for out-of-distribution detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.384718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4cc727ec-376d-4dbf-a356-7078403d6211 · outbound

This paper cites React: Out-of-distribution detection with rectified activa- tions.Advances in Neural Information Processing Sys- tems, 34:144–157,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data React: Out-of-distribution detection with rectified activa- tions.Advances in Neural Information Processing Sys- tems, 34:144–157,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.372451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2acedaa0-278f-4965-9164-82844ce4c01b · outbound

This paper cites Out-of-distribution detection with deep near- est neighbors.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Out-of-distribution detection with deep near- est neighbors

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.361137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6ca1e30e-abd4-4699-a2f5-331a818fd576 · outbound

This paper cites Csi: Novelty detection via contrastive learning on distributionally shifted in- stances.Advances in neural information processing sys- tems, 33:11839–11852,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Csi: Novelty detection via contrastive learning on distributionally shifted in- stances.Advances in neural information processing sys- tems, 33:11839–11852,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.349455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 019c12d3-0942-41b4-a003-dcce3d609190 · outbound

This paper cites Open-Set Recognition: a Good Closed-Set Classifier is All You Need?.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Open-Set Recognition: a Good Closed-Set Classifier is All You Need?

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.981021Z

Source-reported events for the cited work

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Observation 5bc2d8ca-4502-4525-aa80-b32a0503ad43 · outbound

This paper cites Vim: Out-of-distribution with virtual- logit matching.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Vim: Out-of-distribution with virtual- logit matching

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.337894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aec83461-6a52-4845-b857-a6a770f801e4 · outbound

This paper cites Generalized out-of-distribution detec- tion: A survey.International Journal of Computer Vision, 132(12):5635–5662,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Generalized out-of-distribution detec- tion: A survey.International Journal of Computer Vision, 132(12):5635–5662,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.991670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf0cfb44-4266-4f9e-ae38-c9b0afd3c24b · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.995111Z

Source-reported events for the cited work

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Observation 056bd60c-9705-4b1c-a5d7-77f801f83903 · outbound

This paper cites Ctrl: Clus- tering training losses for label error detection.IEEE Trans- actions on Artificial Intelligence,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Ctrl: Clus- tering training losses for label error detection.IEEE Trans- actions on Artificial Intelligence,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.306196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ec2788e7-ac04-4d3b-81bc-b55369a84781 · outbound

This paper cites Wide Residual Networks.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Wide Residual Networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:20.003888Z

Source-reported events for the cited work

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Observation fa5be5e6-c937-48d5-b051-5917683a95c9 · outbound

This paper cites Out-of-distribution detection learning with unreliable out- of-distribution sources.Advances in Neural Information Processing Systems, 36:72110–72123,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Out-of-distribution detection learning with unreliable out- of-distribution sources.Advances in Neural Information Processing Systems, 36:72110–72123,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.295032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c6a742a2-ce4c-4ab6-a877-a226cb74779b · outbound

This paper cites Places: A 10 million image database for scene recognition.IEEE transactions on pattern analysis and machine intelligence, 40(6):1452–1464,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Places: A 10 million image database for scene recognition.IEEE transactions on pattern analysis and machine intelligence, 40(6):1452–1464,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.283393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e7caf53d-1784-4260-96c9-a54c937281f0 · outbound

This paper cites Diversified outlier exposure for out- of-distribution detection via informative extrapolation.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Diversified outlier exposure for out- of-distribution detection via informative extrapolation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.272420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cca61f43-93f7-4dac-816d-d6f4d0f73f70 · outbound

This paper cites an unresolved cited work.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:27:20.261005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7a441b8d-c496-4b8b-9688-178a9463e41c · outbound

This paper cites Letx=v+z i, wherezi∼N(0,σ 2Id×d).

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Letx=v+z i, wherezi∼N(0,σ 2Id×d)

Reference 45

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T20:27:20.130526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f72d01a0-ace5-4150-b319-b1d833eb832f · outbound

This paper cites Extremely Simple Activation Shaping for Out-of-Distribution Detection.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Extremely Simple Activation Shaping for Out-of-Distribution Detection

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.885352Z

Source-reported events for the cited work

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Observation 75e2a0ef-c3f7-4d43-8dfb-0c24bea800f7 · outbound

This paper cites Deep neural networks are easily fooled: High con- fidence predictions for unrecognizable images.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Deep neural networks are easily fooled: High con- fidence predictions for unrecognizable images

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.419202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 189e4239-0712-4e2e-b038-9fdbc18156db · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Imagenet: A large-scale hierarchical image database

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.881633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e4d5327-01e5-40e8-aac1-8c2b8f2f98c6 · outbound

This paper cites Gradient- regularized out-of-distribution detection.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Gradient- regularized out-of-distribution detection

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.408493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7f5920a7-244c-4e42-a241-a6c0b00a2cd1 · outbound

This paper cites Deep Anomaly Detection with Outlier Exposure.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Deep Anomaly Detection with Outlier Exposure

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.911859Z

Source-reported events for the cited work

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Observation 294d49e9-29a0-42be-ba7f-8f3b8ff90798 · outbound

This paper cites Gradorth: a simple yet efficient out-of-distribution detection with or- thogonal projection of gradients.Advances in Neural In- formation Processing Systems, 36,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Gradorth: a simple yet efficient out-of-distribution detection with or- thogonal projection of gradients.Advances in Neural In- formation Processing Systems, 36,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.588292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9e805fc3-e312-4ede-a0a1-3a846f6ef47c · outbound

This paper cites Atom: Robustifying out-of- distribution detection using outlier mining.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Atom: Robustifying out-of- distribution detection using outlier mining

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.577004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aff01ac8-f2e1-4d24-bf8c-0dc36009701a · outbound

This paper cites A closer look at memo- rization in deep networks.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data A closer look at memo- rization in deep networks

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.599326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation db6ce2bd-ee11-484f-b25a-23efb1177582 · outbound

This paper cites Predictive uncertainty estimation via prior networks.Ad- vances in neural information processing systems, 31,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Predictive uncertainty estimation via prior networks.Ad- vances in neural information processing systems, 31,

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.448561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b7633e28-fb52-460d-b0b0-b4cfceccc9c0 · outbound

This paper cites Describing textures in the wild.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Describing textures in the wild

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.877510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39392d3a-b17b-4e27-9ac5-43920367839d · outbound

This paper cites Unknown-aware object detection: Learn- ing what you don’t know from videos in the wild.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Unknown-aware object detection: Learn- ing what you don’t know from videos in the wild

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.553320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ae3eee74-7f21-4b77-95aa-44fd264cb724 · outbound

This paper cites Mitigating neural net- work overconfidence with logit normalization.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Mitigating neural net- work overconfidence with logit normalization

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.326351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7d7ab5e2-85bd-4d92-a584-e6b0e30d98a0 · outbound

This paper cites Discriminative out-of-distribution detection for semantic segmentation.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Discriminative out-of-distribution detection for semantic segmentation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T20:27:19.869518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cff0b620-97f4-4e0f-9644-06adf442aea7 · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153,.

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153,

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:27:20.397500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.