Paper Citation Record · LEDGER

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.03461.

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

pith.paper-citation-record.v1
2506.03461 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:07:49.163877Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b65f698-f38f-4a2d-8622-86c9632bff4f · outbound

This paper cites Me-momentum: Extracting hard confident examplesfromnoisilylabeleddata,in:ProceedingsoftheIEEE/CVF International Conference on Computer Vision (ICCV), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Me-momentum: Extracting hard confident examplesfromnoisilylabeleddata,in:ProceedingsoftheIEEE/CVF International Conference on Computer Vision (ICCV), pp

Reference 1

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Observation f8169285-a13d-4b20-9be5-f57cf0ac5395 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, in: Proceedings of the International Conference on Learning Representations (ICLR).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels An image is worth 16x16 words: Transformers for image recognition at scale, in: Proceedings of the International Conference on Learning Representations (ICLR)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.823675Z

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Observation 27a702fc-a460-4dd5-9728-1624f956c736 · outbound

This paper cites Context-awaremeta-learning,in:Proceedingsofthe International Conference on Learning Representations (ICLR).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Context-awaremeta-learning,in:Proceedingsofthe International Conference on Learning Representations (ICLR)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.810296Z

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Observation a65de40c-6fac-42de-946a-51f6a4d4b32c · outbound

This paper cites Co-teaching:robusttrainingofdeepneuralnet- workswithextremelynoisylabels,in:ProceedingsoftheConference onNeuralInformationProcessingSystems(NeurIPS),p.8536–8546.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Co-teaching:robusttrainingofdeepneuralnet- workswithextremelynoisylabels,in:ProceedingsoftheConference onNeuralInformationProcessingSystems(NeurIPS),p.8536–8546

Reference 4

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Observation 72cf5f8f-c0f4-49ff-aedc-9ff2a1719800 · outbound

This paper cites Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.783339Z

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Observation 623e6767-83fa-4405-b7a0-46032ed44cfd · outbound

This paper cites an unresolved cited work.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:07:53.770032Z

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Observation b14d9402-c053-40cb-a1fa-dd9bd0fc6097 · outbound

This paper cites Rethinking generalization in few-shot classification, in: Proceedings of the Con- ference on Neural Information Processing Systems (NeurIPS), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Rethinking generalization in few-shot classification, in: Proceedings of the Con- ference on Neural Information Processing Systems (NeurIPS), pp

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.756636Z

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Observation a2841b7e-f4d0-4fb2-a47d-6eb280d67e3e · outbound

This paper cites General- purpose in-context learning by meta-learning transformers, in: Pro- ceedings of the Conference on Neural Information Processing Sys- tems (NeurIPS).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels General- purpose in-context learning by meta-learning transformers, in: Pro- ceedings of the Conference on Neural Information Processing Sys- tems (NeurIPS)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.743045Z

Source-reported events for the cited work

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Observation cf9ee965-466c-4f48-b61f-972884795835 · outbound

This paper cites Dividemix:Learningwithnoisyla- belsassemi-supervisedlearning,in:ProceedingsoftheInternational Conference on Learning Representations (ICLR).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Dividemix:Learningwithnoisyla- belsassemi-supervisedlearning,in:ProceedingsoftheInternational Conference on Learning Representations (ICLR)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.729922Z

Source-reported events for the cited work

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Observation 7e5456b8-d305-441f-9484-7988bf1472aa · outbound

This paper cites Few- shot learning with noisy labels, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Few- shot learning with noisy labels, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.716424Z

Source-reported events for the cited work

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Observation d3df6ae4-3f56-4bd9-8b84-ecba4d31d7c1 · outbound

This paper cites Early-learning regularization prevents memorization of noisy labels, in: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Early-learning regularization prevents memorization of noisy labels, in: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), pp

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.702575Z

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Observation 52256791-a082-490b-ac38-3543f6ee3c4c · outbound

This paper cites Peerlossfunctions:learningfromnoisylabels without knowing noise rates, in: Proceedings of the International Conference on Machine Learning (ICML), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Peerlossfunctions:learningfromnoisylabels without knowing noise rates, in: Proceedings of the International Conference on Machine Learning (ICML), pp

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.688866Z

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Observation 57b6873c-8a9f-484d-aee2-9766140d3404 · outbound

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RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation 113741f9-5736-4524-a642-6599f9b88f48 · outbound

This paper cites Rnnp: A robust few-shot learning approach, in: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Rnnp: A robust few-shot learning approach, in: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.381601Z

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Observation 43b45fbb-f671-4338-a459-05575fca0d48 · outbound

This paper cites A simple neural attentive meta-learner, in: Proceedings of the International Conference on Learning Representations (ICLR).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels A simple neural attentive meta-learner, in: Proceedings of the International Conference on Learning Representations (ICLR)

Reference 15

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Observation 03968015-bd02-4bc7-ab3c-b30057a196ac · outbound

This paper cites Pervasivelabelerrors in test sets destabilize machine learning benchmarks, in: Proceed- ings of the Conference on Neural Information Processing Systems (NeurIPS).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Pervasivelabelerrors in test sets destabilize machine learning benchmarks, in: Proceed- ings of the Conference on Neural Information Processing Systems (NeurIPS)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:52.921408Z

Source-reported events for the cited work

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Observation 4fd8fbce-cf71-414a-afcb-c7f8624e7706 · outbound

This paper cites Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:52.704840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94338c3d-e20c-4a58-aea8-54d34d2f83af · outbound

This paper cites Meta-learning for semi- supervisedfew-shotclassification,in:ProceedingsoftheInternational Conference on Learning Representations (ICLR).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Meta-learning for semi- supervisedfew-shotclassification,in:ProceedingsoftheInternational Conference on Learning Representations (ICLR)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:52.493020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c81dc40d-1a43-4152-86fc-60bbe0a3b8b6 · outbound

This paper cites Learning with symmetric label noise: the importance of being unhinged, in: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Learning with symmetric label noise: the importance of being unhinged, in: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), pp

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:52.314193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c3b7fbb-e8ef-4609-b653-e195f52de514 · outbound

This paper cites The balanced-pairwise-affinities feature transform, in: Proceedings of the International Conference on Machine Learning (ICML), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels The balanced-pairwise-affinities feature transform, in: Proceedings of the International Conference on Machine Learning (ICML), pp

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:52.114325Z

Source-reported events for the cited work

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Observation 9717f8b5-51f1-4f99-8886-39b772ba4366 · outbound

This paper cites Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:51.906026Z

Source-reported events for the cited work

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Observation 5c81b15f-0efa-4b47-ba57-704b6e175920 · outbound

This paper cites Prototypical networks for few-shot learning, in: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Prototypical networks for few-shot learning, in: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:51.658890Z

Source-reported events for the cited work

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Observation bb52a0a7-4d1d-4f83-a418-10f8db5b2c8b · outbound

This paper cites Learning to rectifyforrobustlearningwithnoisylabels.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Learning to rectifyforrobustlearningwithnoisylabels

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:51.365291Z

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Observation 4cbdf170-fc3c-49a6-b585-3d3801c994c3 · outbound

This paper cites From imagenet to image classification: Contextualizing progress on benchmarks, in: Proceedings of the International Conference on Ma- chine Learning (ICML), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels From imagenet to image classification: Contextualizing progress on benchmarks, in: Proceedings of the International Conference on Ma- chine Learning (ICML), pp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:51.107368Z

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Observation 73b5b66f-3e94-4062-a337-79b6ed7b8398 · outbound

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RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:07:50.851812Z

Source-reported events for the cited work

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Observation 565183a7-8a30-4a09-8fe2-ae889a81cc7e · outbound

This paper cites an unresolved cited work.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:07:50.338388Z

Source-reported events for the cited work

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Observation 531ec87b-c12b-4147-8260-27dc75421841 · outbound

This paper cites an unresolved cited work.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:07:50.200128Z

Source-reported events for the cited work

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Observation 41cc8978-5c64-4cb9-8e97-1967279bebbb · outbound

This paper cites Fine-grained classification with noisy labels, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Fine-grained classification with noisy labels, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:50.088574Z

Source-reported events for the cited work

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Observation 4023a4d0-087c-477d-8793-53f773144d6d · outbound

This paper cites Learningtopurify noisy labels via meta soft label corrector.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Learningtopurify noisy labels via meta soft label corrector

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:49.941015Z

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Observation 258fc894-d8fb-490d-b555-e3912d8179ba · outbound

This paper cites Object detection as a positive- unlabeled problem, in: Proceedings of the British Machine Vision Conference (BMVC).

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Object detection as a positive- unlabeled problem, in: Proceedings of the British Machine Vision Conference (BMVC)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:49.790144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f37910eb-17a8-4d6c-b4bd-d997f767b89b · outbound

This paper cites Understandingdeeplearning(still)requiresrethinkinggeneralization.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Understandingdeeplearning(still)requiresrethinkinggeneralization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:49.621809Z

Source-reported events for the cited work

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Observation 167f300e-4943-481f-bebd-5a8deb3b9888 · outbound

This paper cites Shallow bayesian meta learning for real-world few-shot recognition.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Shallow bayesian meta learning for real-world few-shot recognition

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:49.502419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 59a0d6e0-10bb-4683-afbc-ad73e27b06db · outbound

This paper cites Meta label correction for noisy label learning.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels Meta label correction for noisy label learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:49.329175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63315177-481c-4b0c-ab54-a87fed72a081 · outbound

This paper cites 3637–3645.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels 3637–3645

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:50.484159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e5f0df5b-a05f-45b7-8611-0b283430d067 · outbound

This paper cites 6543–6553.

RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels 6543–6553

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:53.590734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.