Paper Citation Record · LEDGER

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise

As of 19 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 3 inbound Pith citation observations for arXiv:2501.01130.

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

pith.paper-citation-record.v1
2501.01130 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:46:05.914244Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T23:16:28.400563Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:25:45.591475Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy40
unresolved10
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 244465d5-f55a-4877-af5d-f5dbff17a072 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Imagenet classification with deep convolutional neural networks,

Reference 1

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Observation 70309e83-a69b-46ab-aa9a-4e925ba9e151 · outbound

This paper cites You only look once: Unified, real-time object detection,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise You only look once: Unified, real-time object detection,

Reference 2

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Observation 46989b89-25e7-44ed-acd4-aafeeb2f2c0b · outbound

This paper cites Deep learning over multi-field categorical data: –a case study on user response prediction,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Deep learning over multi-field categorical data: –a case study on user response prediction,

Reference 3

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Observation 874c6379-31c8-4608-a3b9-db02cc31174d · outbound

This paper cites Neural information retrieval: at the end of the early years,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Neural information retrieval: at the end of the early years,

Reference 4

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Observation 68267083-a5c6-4ca5-8478-201d265f3a85 · outbound

This paper cites Universal language model fine-tuning for text classification,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Universal language model fine-tuning for text classification,

Reference 5

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Observation c11864a6-6aa0-4db6-8c2f-e8331c8abd33 · outbound

This paper cites Twitter sentiment analysis with deep convolutional neural networks,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Twitter sentiment analysis with deep convolutional neural networks,

Reference 6

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Observation 7e357988-93e0-4394-92fb-b8217fe11354 · outbound

This paper cites Supervised contrastive learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Supervised contrastive learning,

Reference 7

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Observation b693ef33-cd47-48c5-a364-ec1f5d0a1016 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise A simple framework for contrastive learning of visual representations,

Reference 8

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Observation cbdd8927-e442-4d6d-964e-484a7d534f61 · outbound

This paper cites Multi-objective interpolation training for robustness to label noise,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Multi-objective interpolation training for robustness to label noise,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.442851Z

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Observation 004d6c4d-9f6d-423e-9ed0-06e5fc72a5b9 · outbound

This paper cites Unicon: Combating label noise through uniform selection and contrastive learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Unicon: Combating label noise through uniform selection and contrastive learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.431440Z

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Observation ec96688b-028e-4c6a-b019-164a78db069d · outbound

This paper cites Selective-supervised contrastive learning with noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Selective-supervised contrastive learning with noisy labels,

Reference 11

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Observation 8485f373-bac0-4b41-93e8-908f7e5635ad · outbound

This paper cites Twin contrastive learning with noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Twin contrastive learning with noisy labels,

Reference 12

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Observation 06b09599-6691-4418-af1b-12960103543d · outbound

This paper cites Pico: Contrastive label disambiguation for partial label learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Pico: Contrastive label disambiguation for partial label learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.396860Z

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Observation 730fdbb7-8348-4caf-b422-f4ec15d200e6 · outbound

This paper cites Pico+: Contrastive label disambiguation for robust partial label learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Pico+: Contrastive label disambiguation for robust partial label learning,

Reference 14

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Observation 42fa94a0-9520-4e3b-935b-23fc78ef2c5b · outbound

This paper cites Leveraged weighted loss for partial label learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Leveraged weighted loss for partial label learning,

Reference 15

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Observation 69d5c811-623c-488b-94c6-f1b570dedba0 · outbound

This paper cites Class-aware contrastive semi-supervised learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Class-aware contrastive semi-supervised learning,

Reference 16

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Observation d2cb5d91-5707-4cb4-b931-fcb192d75f73 · outbound

This paper cites A graph-theoretic framework for understanding open-world semi-supervised learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise A graph-theoretic framework for understanding open-world semi-supervised learning,

Reference 17

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Observation 3931bd81-e378-440a-93e5-8decd11af5fc · outbound

This paper cites Rethinking weak supervision in helping contrastive learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Rethinking weak supervision in helping contrastive learning,

Reference 18

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Observation 3cb31e88-37f6-4bb6-97b2-14260a56cd09 · outbound

This paper cites Constrained mean shift using distant yet related neighbors for representation learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Constrained mean shift using distant yet related neighbors for representation learning,

Reference 19

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Observation 896ddbe2-440f-4b15-adb9-89901c6da21c · outbound

This paper cites Robust contrastive learning against noisy views,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Robust contrastive learning against noisy views,

Reference 20

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Observation f60b6d0c-81da-48c1-be72-80f50e5e886e · outbound

This paper cites Training deep neural-networks using a noise adaptation layer,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Training deep neural-networks using a noise adaptation layer,

Reference 21

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Observation 2d67a800-a7c4-4398-bb35-895194cdd208 · outbound

This paper cites Masking: A new perspective of noisy supervision,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Masking: A new perspective of noisy supervision,

Reference 22

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Observation 634563c1-b928-4d2b-9e47-00f01d483539 · outbound

This paper cites Deep learning from noisy image labels with quality embedding,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Deep learning from noisy image labels with quality embedding,

Reference 23

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Observation 3d7a5e03-ba4d-4abc-9cc7-1b99e3678e27 · outbound

This paper cites Does label smoothing mitigate label noise?.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Does label smoothing mitigate label noise?

Reference 24

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Observation 31b184fc-64ef-4b3d-8fe9-8adce946946e · outbound

This paper cites Regularizing Neural Networks by Penalizing Confident Output Distributions.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Regularizing Neural Networks by Penalizing Confident Output Distributions

Reference 25

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Observation 26a85ec7-3bc0-4620-9ad2-73451ea038f1 · outbound

This paper cites Open-set label noise can improve robustness against inherent label noise,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Open-set label noise can improve robustness against inherent label noise,

Reference 26

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Observation ccb764db-8b96-4f4e-a56b-a6d4ec0815ac · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 27

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Observation 04632660-4e1d-47ec-b618-ac290aad75af · outbound

This paper cites Selfie: Refurbishing unclean samples for robust deep learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Selfie: Refurbishing unclean samples for robust deep learning,

Reference 28

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Observation fc72b0c8-ca9c-40f2-8779-7e9fb6d272ee · outbound

This paper cites How does disagreement help generalization against label corruption?.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise How does disagreement help generalization against label corruption?

Reference 29

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Observation d6315df2-bdb8-4e1e-aa48-a7a88701e531 · outbound

This paper cites Iterative learning with open-set noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Iterative learning with open-set noisy labels,

Reference 30

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Observation 41eb7034-89e5-422c-acce-205d1283f6d9 · outbound

This paper cites Using trusted data to train deep networks on labels corrupted by severe noise,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Using trusted data to train deep networks on labels corrupted by severe noise,

Reference 31

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Observation d9148f0e-a478-406f-a85f-84f59ce3ba85 · outbound

This paper cites Dimensionality-driven learning with noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Dimensionality-driven learning with noisy labels,

Reference 32

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Observation 396f8fff-dba7-465e-a581-57b961a59e9e · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Making deep neural networks robust to label noise: A loss correction approach,

Reference 33

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Observation 2788750d-534d-4253-95b8-91ea53473c0d · outbound

This paper cites Approximating Instance-Dependent Noise via Instance-Confidence Embedding.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Approximating Instance-Dependent Noise via Instance-Confidence Embedding

Reference 34

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Observation 7f231c0a-c096-4e60-a6f9-58c362340e94 · outbound

This paper cites Robust loss functions under label noise for deep neural networks,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Robust loss functions under label noise for deep neural networks,

Reference 35

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Observation bc5916f8-15d4-4014-a67f-4083c8057070 · outbound

This paper cites Normalized loss functions for deep learning with noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Normalized loss functions for deep learning with noisy labels,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.141576Z

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Observation b13ec5d0-e72c-4e7e-bf57-afef51bb57c0 · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Symmetric cross entropy for robust learning with noisy labels,

Reference 37

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Observation f8bb37d5-62c5-415a-9c71-1abf2a0af1bc · outbound

This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 38

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Observation a277c999-06f5-4a1f-83c0-5c8951dcd664 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Momentum contrast for unsupervised visual representation learning,

Reference 39

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Observation bf333d1e-c645-4c82-8aea-b2119a825c00 · outbound

This paper cites Residual relaxation for multi-view representation learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Residual relaxation for multi-view representation learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.097700Z

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Observation ac457146-040f-4d5d-8dce-42627d92b889 · outbound

This paper cites Noise is also useful: Negative correlation-steered latent contrastive learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Noise is also useful: Negative correlation-steered latent contrastive learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.083659Z

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Observation 72d32688-cfc4-4ab2-b591-01a4f907879f · outbound

This paper cites Mitigating Memorization of Noisy Labels via Regularization between Representations.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Mitigating Memorization of Noisy Labels via Regularization between Representations

Reference 42

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Observation 1cd40221-ed41-4ce6-a09e-85348b004254 · outbound

This paper cites Investigating why contrastive learning benefits robustness against label noise,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Investigating why contrastive learning benefits robustness against label noise,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.071168Z

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Observation 93aa3ef1-4e60-4214-b653-997f82eee763 · outbound

This paper cites A theoretical analysis of contrastive unsupervised representation learning,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise A theoretical analysis of contrastive unsupervised representation learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.058368Z

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Observation 4ce9f9f3-85b3-4866-8d5f-816204d36d39 · outbound

This paper cites Learning with noisy labels,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Learning with noisy labels,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.045470Z

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Observation 76a22691-dfee-4129-9354-b743f002340d · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Understanding contrastive representation learning through alignment and uniformity on the hypersphere,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.033025Z

Source-reported events for the cited work

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Observation abdf58f5-3780-4f63-96e1-6a028f89385d · outbound

This paper cites Learning multiple layers of features from tiny images,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Learning multiple layers of features from tiny images,

Reference 47

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Observation 7ddd8f3e-2ced-4334-bfc4-df9072ff128f · outbound

This paper cites Learning from massive noisy labeled data for image classification,.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Learning from massive noisy labeled data for image classification,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.009622Z

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Observation f0071c16-bc56-4c42-b6fb-9657730e36d2 · outbound

This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 49

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Observation d4811f89-b5fb-44b7-aea1-e19c9d6accdb · outbound

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An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Unresolved cited work

Reference 50

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Pith citing papers

Observation 2151bdbd-7114-4750-bd2e-c09ea711acd4 · inbound

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning cites this paper.

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:30:58.291549Z

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Observation 18d37b8b-7951-47bd-a5c8-da0ad5f06603 · inbound

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning cites this paper.

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:31:19.394724Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 345b14e0-150c-4038-aad9-bbdc4456611f · inbound

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning cites this paper.

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:25:45.593113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.