Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:26:58.411597Z
Paper Citation Record · LEDGER
Learning from Ambiguous Data with Hard Labels
As of 16 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2501.01844.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
58 of 58 outbound references displayed
- verified exact0
- verified fuzzy51
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 90362a7d-391e-4274-9c7c-9ed474eb3c8f · outbound
This paper cites A survey on data collection for ma- chine learning: a big data-ai integration perspective,.
Learning from Ambiguous Data with Hard Labels A survey on data collection for ma- chine learning: a big data-ai integration perspective,
Reference 1
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 178d4f20-1f92-456d-9d13-221c8b438485 · outbound
This paper cites Crowdsourced data management: A survey,.
Learning from Ambiguous Data with Hard Labels Crowdsourced data management: A survey,
Reference 2
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c668ea93-c2fa-4d9c-a4f6-cc3c2324bdd4 · outbound
This paper cites Learning from crowdsourced labeled data: a survey,.
Learning from Ambiguous Data with Hard Labels Learning from crowdsourced labeled data: a survey,
Reference 3
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c347e476-b047-4a97-b6d2-fa6c1b01ae93 · outbound
This paper cites Learning from multiple annotators with varying expertise,.
Learning from Ambiguous Data with Hard Labels Learning from multiple annotators with varying expertise,
Reference 4
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 88294b31-165c-4212-98e4-f7b416e243cc · outbound
This paper cites Learning from noisy large-scale datasets with minimal supervision,.
Learning from Ambiguous Data with Hard Labels Learning from noisy large-scale datasets with minimal supervision,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:59.040603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ad9629c7-7508-4aca-b05e-ffd5fe0b01a8 · outbound
This paper cites Evaluating machine accuracy on imagenet,.
Learning from Ambiguous Data with Hard Labels Evaluating machine accuracy on imagenet,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:59.031387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 88ea76c9-a30c-4803-ab74-84e1d80d925e · outbound
This paper cites Learning with noisy labels revisited: A study using real-world human annotations,.
Learning from Ambiguous Data with Hard Labels Learning with noisy labels revisited: A study using real-world human annotations,
Reference 7
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a7109247-e224-4017-af9a-6195ef5ba6aa · outbound
This paper cites Pervasive label errors in test sets destabilize machine learning benchmarks,.
Learning from Ambiguous Data with Hard Labels Pervasive label errors in test sets destabilize machine learning benchmarks,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:59.010277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation af5f2d18-b5f1-4809-add8-920c0a35dd6f · outbound
This paper cites Deep label distribution learning with label ambiguity,.
Learning from Ambiguous Data with Hard Labels Deep label distribution learning with label ambiguity,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.998953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 65afd91c-660c-41a2-b6fb-73ef5ab8bd57 · outbound
This paper cites Learning Soft Labels via Meta Learning.
Learning from Ambiguous Data with Hard Labels Learning Soft Labels via Meta Learning
Reference 10
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 99a22f18-3269-4b4f-9558-55eb466c7a6f · outbound
This paper cites Human uncertainty makes classification more robust,.
Learning from Ambiguous Data with Hard Labels Human uncertainty makes classification more robust,
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d15449aa-11fe-487c-ad73-3d50f9de13d0 · outbound
This paper cites Re-labeling imagenet: from single to multi-labels, from global to localized labels,.
Learning from Ambiguous Data with Hard Labels Re-labeling imagenet: from single to multi-labels, from global to localized labels,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.978317Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1ccf8fbf-57e3-4536-b2d1-1409505b2b20 · outbound
This paper cites Artificial neural variability for deep learning: On overfitting, noise memorization, and catastrophic forgetting,.
Learning from Ambiguous Data with Hard Labels Artificial neural variability for deep learning: On overfitting, noise memorization, and catastrophic forgetting,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.967154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2de81406-2841-4596-bebe-8944160f217f · outbound
This paper cites Positive-negative momen- tum: Manipulating stochastic gradient noise to improve generalization,.
Learning from Ambiguous Data with Hard Labels Positive-negative momen- tum: Manipulating stochastic gradient noise to improve generalization,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.955678Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 95881082-9cb2-41e8-a5f4-e39c0b6ec12a · outbound
This paper cites Beyond Hard Labels: Investigating data label distributions.
Learning from Ambiguous Data with Hard Labels Beyond Hard Labels: Investigating data label distributions
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:26:58.247443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63d05169-aa64-4f71-b0eb-dce27d65fe1f · outbound
This paper cites Sparse double descent: Where network pruning aggravates overfitting,.
Learning from Ambiguous Data with Hard Labels Sparse double descent: Where network pruning aggravates overfitting,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.944831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5e96410f-c717-47bc-ba45-0749c1d873c3 · outbound
This paper cites A Survey of Label-noise Representation Learning: Past, Present and Future.
Learning from Ambiguous Data with Hard Labels A Survey of Label-noise Representation Learning: Past, Present and Future
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:26:58.254954Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5c69136d-8bbb-4875-a20d-3bf790ef3059 · outbound
This paper cites Analysis of learning from positive and unlabeled data,.
Learning from Ambiguous Data with Hard Labels Analysis of learning from positive and unlabeled data,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.934038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8849afa8-5b4d-409b-90a3-d5946c260f7e · outbound
This paper cites Convex formulation for learning from positive and unlabeled data,.
Learning from Ambiguous Data with Hard Labels Convex formulation for learning from positive and unlabeled data,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.923252Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 12329261-0bb5-433c-a812-0265c4e464e2 · outbound
This paper cites Positive- unlabeled learning with non-negative risk estimator,.
Learning from Ambiguous Data with Hard Labels Positive- unlabeled learning with non-negative risk estimator,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.912455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 331980ed-2e2e-434c-8552-7259cf8591ab · outbound
This paper cites Theoreti- cal comparisons of positive-unlabeled learning against positive-negative learning,.
Learning from Ambiguous Data with Hard Labels Theoreti- cal comparisons of positive-unlabeled learning against positive-negative learning,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.902540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 932fb47b-4c1f-49ad-8f4e-d27ad8d418f5 · outbound
This paper cites Learning from corrupted binary labels via class-probability estimation,.
Learning from Ambiguous Data with Hard Labels Learning from corrupted binary labels via class-probability estimation,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.891375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 332b6562-3507-4231-8623-41aea96d4f10 · outbound
This paper cites Estimating the class prior and posterior from noisy positives and unlabeled data,.
Learning from Ambiguous Data with Hard Labels Estimating the class prior and posterior from noisy positives and unlabeled data,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.880096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f6d16479-b954-43a0-81fb-4ef636c85841 · outbound
This paper cites Class-prior estimation for learning from positive and unlabeled data,.
Learning from Ambiguous Data with Hard Labels Class-prior estimation for learning from positive and unlabeled data,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.869829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 56944038-d87b-4468-9268-596a07a67b6c · outbound
This paper cites Rethinking class-prior estimation for positive-unlabeled learning,.
Learning from Ambiguous Data with Hard Labels Rethinking class-prior estimation for positive-unlabeled learning,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.858756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bd99160e-dc77-479d-97d7-c8132f74e51c · outbound
This paper cites Generalized jensen-shannon divergence loss for learning with noisy labels,.
Learning from Ambiguous Data with Hard Labels Generalized jensen-shannon divergence loss for learning with noisy labels,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.848010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cad82bbe-7519-449c-b5e1-fd0a2c05e312 · outbound
This paper cites mixup: Beyond empirical risk minimization,.
Learning from Ambiguous Data with Hard Labels mixup: Beyond empirical risk minimization,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.837166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c6578fda-c743-4288-a3b3-5ffbaa0cd353 · outbound
This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,.
Learning from Ambiguous Data with Hard Labels Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.816009Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 826ac8f9-51b1-4e78-bc9e-beb16aa0f012 · outbound
This paper cites Deep residual learning for image recognition,.
Learning from Ambiguous Data with Hard Labels Deep residual learning for image recognition,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.806837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 69b2dfa8-b0fb-4c8d-9cbe-c09c56dce18e · outbound
This paper cites Training Deep Neural Networks on Noisy Labels with Bootstrapping.
Learning from Ambiguous Data with Hard Labels Training Deep Neural Networks on Noisy Labels with Bootstrapping
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:26:58.310538Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51b52c4f-4a41-4e4b-a30c-2481fe3db80f · outbound
This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels,.
Learning from Ambiguous Data with Hard Labels Generalized cross entropy loss for training deep neural networks with noisy labels,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.795297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5ef40cd5-e4f8-42f2-9a90-261039906c2b · outbound
This paper cites Symmetric cross entropy for robust learning with noisy labels,.
Learning from Ambiguous Data with Hard Labels Symmetric cross entropy for robust learning with noisy labels,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.784703Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bd8909ad-ff08-4e64-92f2-4a75f62ff695 · outbound
This paper cites L dmi: A novel information- theoretic loss function for training deep nets robust to label noise,.
Learning from Ambiguous Data with Hard Labels L dmi: A novel information- theoretic loss function for training deep nets robust to label noise,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.774186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2a5d5f73-2ac7-4047-86d1-07f17da2cdb8 · outbound
This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels,.
Learning from Ambiguous Data with Hard Labels Co-teaching: Robust training of deep neural networks with extremely noisy labels,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.763271Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f00879b4-3c22-49b6-b0b4-f6c4c4273361 · outbound
This paper cites How does disagreement help generalization against label corruption?.
Learning from Ambiguous Data with Hard Labels How does disagreement help generalization against label corruption?
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.752639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e60fa32f-4f05-4ff3-88fe-bff9612dd9e8 · outbound
This paper cites Robust early-learning: Hindering the memorization of noisy labels,.
Learning from Ambiguous Data with Hard Labels Robust early-learning: Hindering the memorization of noisy labels,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.741606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5b1a8df6-18fb-4c1b-a6eb-1521bb13f12a · outbound
This paper cites Early- learning regularization prevents memorization of noisy labels,.
Learning from Ambiguous Data with Hard Labels Early- learning regularization prevents memorization of noisy labels,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.731527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 66d2ca1e-ab97-4a18-bc15-c097fb16e180 · outbound
This paper cites Dividemix: Learning with noisy labels as semi-supervised learning,.
Learning from Ambiguous Data with Hard Labels Dividemix: Learning with noisy labels as semi-supervised learning,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.721764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 68a85159-27cd-4470-a0f1-3d17d8788a8d · outbound
This paper cites Understanding and improving early stopping for learning with noisy labels,.
Learning from Ambiguous Data with Hard Labels Understanding and improving early stopping for learning with noisy labels,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.709744Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cb5777ac-42fb-4dac-b3c2-035d2280dfa3 · outbound
This paper cites Fixmatch: Simplifying semi- supervised learning with consistency and confidence,.
Learning from Ambiguous Data with Hard Labels Fixmatch: Simplifying semi- supervised learning with consistency and confidence,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.697539Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7b1377c9-cd00-41dc-972f-f9f668446d15 · outbound
This paper cites Exploiting spatial dimen- sions of latent in gan for real-time image editing,.
Learning from Ambiguous Data with Hard Labels Exploiting spatial dimen- sions of latent in gan for real-time image editing,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.686858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0f8ebfdc-aee7-4863-99a4-c9ab7a104b35 · outbound
This paper cites Stargan v2: Diverse image synthesis for multiple domains,.
Learning from Ambiguous Data with Hard Labels Stargan v2: Diverse image synthesis for multiple domains,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.676077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3560d9c4-0c8d-4496-a607-84277cfd9ca1 · outbound
This paper cites Variational label enhancement,.
Learning from Ambiguous Data with Hard Labels Variational label enhancement,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.665193Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 26115eca-d13d-4b77-a579-f19d226b6155 · outbound
This paper cites Are we done with ImageNet?.
Learning from Ambiguous Data with Hard Labels Are we done with ImageNet?
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:26:58.363303Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ab5f321-bcec-4a24-a5f7-b183a2fdf09e · outbound
This paper cites From imagenet to image classification: Contextualizing progress on bench- marks,.
Learning from Ambiguous Data with Hard Labels From imagenet to image classification: Contextualizing progress on bench- marks,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.654097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f241f3ef-45dc-4dca-b1d0-4fe5484e45c6 · outbound
This paper cites A data-centric approach for improving ambiguous labels with combined semi-supervised classifica- tion and clustering,.
Learning from Ambiguous Data with Hard Labels A data-centric approach for improving ambiguous labels with combined semi-supervised classifica- tion and clustering,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.643548Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 86437d81-3398-4db7-b6b5-517537a58458 · outbound
This paper cites Binary classification with ambiguous training data,.
Learning from Ambiguous Data with Hard Labels Binary classification with ambiguous training data,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.633292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9ecf7304-4c80-4d73-8e5a-0a2b76c93742 · outbound
This paper cites Learning from partial labels,.
Learning from Ambiguous Data with Hard Labels Learning from partial labels,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.620760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4908635b-3474-4bda-90f9-fb10312e1776 · outbound
This paper cites Solving the partial label learning problem: An instance-based approach,.
Learning from Ambiguous Data with Hard Labels Solving the partial label learning problem: An instance-based approach,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.607422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4dc8ad0e-80df-4fdc-8676-6f27791b46ec · outbound
This paper cites Disambiguation-free partial label learning,.
Learning from Ambiguous Data with Hard Labels Disambiguation-free partial label learning,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.594580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 005dbaf3-c932-4d83-8bf1-e899fea599c3 · outbound
This paper cites Learning with Instance-Dependent Label Noise: A Sample Sieve Approach.
Learning from Ambiguous Data with Hard Labels Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:26:58.386101Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6f47e53-c059-4e73-a2c7-bdde2c215ab6 · outbound
This paper cites Part-dependent label noise: Towards instance-dependent label noise,.
Learning from Ambiguous Data with Hard Labels Part-dependent label noise: Towards instance-dependent label noise,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.582243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5e0d2f99-0157-4cc9-ba28-dd091d19ff02 · outbound
This paper cites Label distribution learning,.
Learning from Ambiguous Data with Hard Labels Label distribution learning,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.570312Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cbbec5ce-26c2-4a1c-b2ce-623ea92d5e77 · outbound
This paper cites When does label smoothing help?.
Learning from Ambiguous Data with Hard Labels When does label smoothing help?
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.557735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 74eb7099-9611-419f-8565-558fd20666d8 · outbound
This paper cites How Does Mixup Help With Robustness and Generalization?.
Learning from Ambiguous Data with Hard Labels How Does Mixup Help With Robustness and Generalization?
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:26:58.402963Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 716bb8df-cfad-405a-8fdb-1497d6f1dc93 · outbound
This paper cites Each animal category in the dataset has its own distinct visual style, and the images were carefully curated and labeled to ensure high standards.
Learning from Ambiguous Data with Hard Labels Each animal category in the dataset has its own distinct visual style, and the images were carefully curated and labeled to ensure high standards
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.545422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8d67d964-7f86-4cc5-b5f0-99219675faa4 · outbound
This paper cites To perform local editing on AFHQ, we randomly pair images from the training set and select half-and-half masks that divide them equally into horizontal and vertical halves (see Fig.
Learning from Ambiguous Data with Hard Labels To perform local editing on AFHQ, we randomly pair images from the training set and select half-and-half masks that divide them equally into horizontal and vertical halves (see Fig
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.532005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9d3e0804-3cec-429f-98fa-7cf510883d83 · outbound
This paper cites Available: https://openreview.net/forum?id=r1Ddp1-Rb 3.
Learning from Ambiguous Data with Hard Labels Available: https://openreview.net/forum?id=r1Ddp1-Rb 3
Reference 2018
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:26:58.826078Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.