Why Can Accurate Models Be Learned from Inaccurate Annotations? | Paper Citation Record

Observation 3d1421ae-4dce-46ba-bc5f-51a30ec97952 · outbound

This paper cites Is your noise correction noisy? pls: Robustness to label noise with two stage detection.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Is your noise correction noisy? pls: Robustness to label noise with two stage detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.920240Z

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Observation f11c3857-11fc-479e-b714-70eb8131fd32 · outbound

This paper cites Understand- ing and improving early stopping for learning with noisy la- bels.Advances in Neural Information Processing Systems, 34:24392–24403, 2021.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Understand- ing and improving early stopping for learning with noisy la- bels.Advances in Neural Information Processing Systems, 34:24392–24403, 2021

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.903702Z

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Observation e41ad858-ed24-442b-9b25-e3e4e9319cc4 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.885233Z

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Observation 0e9fb373-2ee6-4f03-9126-b9b2ff397b55 · outbound

This paper cites Learning from ambiguously labeled images.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Learning from ambiguously labeled images

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.868380Z

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Observation d6738e71-6ddc-48a6-9df2-017a0a5bf7aa · outbound

This paper cites Learning from partial labels.J.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Learning from partial labels.J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.851249Z

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This paper cites The rotation of eigenvectors by a perturbation.

Why Can Accurate Models Be Learned from Inaccurate Annotations? The rotation of eigenvectors by a perturbation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.834545Z

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Observation f77c995d-80eb-4d23-afff-5ca579d1fbdd · outbound

This paper cites Leveraging latent label distributions for partial label learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Leveraging latent label distributions for partial label learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.817706Z

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Observation 9875d28d-f4d0-44d0-abda-7df79f3676fd · outbound

This paper cites Partial label learning with self-guided retraining.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Partial label learning with self-guided retraining

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.800030Z

Source-reported events for the cited work

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Observation 59b0e882-b409-46a2-99a0-a4ae04d70387 · outbound

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

Why Can Accurate Models Be Learned from Inaccurate Annotations? Robust loss functions under label noise for deep neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.780796Z

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Observation 8360bc68-5624-4766-aa91-5aba53cd7414 · outbound

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

Why Can Accurate Models Be Learned from Inaccurate Annotations? Training deep neural-networks using a noise adaptation layer

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.761681Z

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Observation 8521e832-66bd-4c0f-b112-4e5d282c6d89 · outbound

This paper cites Isaac newton, philosophiae natu- ralis principia mathematica, (1687).

Why Can Accurate Models Be Learned from Inaccurate Annotations? Isaac newton, philosophiae natu- ralis principia mathematica, (1687)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.743149Z

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Observation 98ab5a74-c559-4ae3-8375-9e5c02a4ea65 · outbound

This paper cites Multiple instance metric learning from automatically labeled bags of faces.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Multiple instance metric learning from automatically labeled bags of faces

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.724735Z

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Observation e897c848-eaa2-49da-ba07-cc264e58769c · outbound

This paper cites Co- teaching: Robust training of deep neural networks with ex- tremely noisy labels.Advances in neural information pro- cessing systems, 31, 2018.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Co- teaching: Robust training of deep neural networks with ex- tremely noisy labels.Advances in neural information pro- cessing systems, 31, 2018

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.705999Z

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Observation 852a8ed6-0ebf-4577-95f5-a9b3bc48e8c2 · outbound

This paper cites Svdiff: Compact param- eter space for diffusion fine-tuning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Svdiff: Compact param- eter space for diffusion fine-tuning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.688194Z

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Observation 35be8a47-b095-4091-96da-84d06d605e41 · outbound

This paper cites Deep residual learning for image recognition.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Deep residual learning for image recognition

Reference 15

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Observation b736ee73-8f1d-4ead-8743-381f85c9e729 · outbound

This paper cites Partial label learning with semantic label representations.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Partial label learning with semantic label representations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.660623Z

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Observation a3fcb334-0f3c-4d84-836b-a0b5d7374894 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Why Can Accurate Models Be Learned from Inaccurate Annotations? LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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Observation 878a261d-4fc0-4b24-aa6b-166c6e676919 · outbound

This paper cites O2u- net: A simple noisy label detection approach for deep neu- ral networks.

Why Can Accurate Models Be Learned from Inaccurate Annotations? O2u- net: A simple noisy label detection approach for deep neu- ral networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.643621Z

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Observation a2e26792-bcf7-4cd6-8ba1-e9f72a2ed3f4 · outbound

This paper cites Huiskes and Michael S.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Huiskes and Michael S

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.628603Z

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Observation 23a7e912-fee9-472c-b193-61f9a31870b1 · outbound

This paper cites Learning from am- biguously labeled examples.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Learning from am- biguously labeled examples

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.609711Z

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Observation 8e70fca1-2204-430a-94e2-0607c0a37a8a · outbound

This paper cites Complementary Classifier Induced Partial Label Learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Complementary Classifier Induced Partial Label Learning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:10:18.921829Z

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Observation 5ebb5239-ad8c-49fc-81fc-feb507c5be39 · outbound

This paper cites Partial la- bel learning with dissimilarity propagation guided candidate label shrinkage.Advances in neural information processing systems, 36:34190–34200, 2023.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Partial la- bel learning with dissimilarity propagation guided candidate label shrinkage.Advances in neural information processing systems, 36:34190–34200, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.589684Z

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Observation d997b98f-4bcc-48e7-a925-2b3a7bfc4ebf · outbound

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

Why Can Accurate Models Be Learned from Inaccurate Annotations? Learning multiple layers of features from tiny images

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.572065Z

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Observation 2012b6bd-58e1-4ba5-975d-09b969010b56 · outbound

This paper cites Learning to learn from noisy labeled data.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Learning to learn from noisy labeled data

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.554933Z

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Observation 1094d3c7-9e19-48ce-a19b-d12e2b6469d3 · outbound

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

Why Can Accurate Models Be Learned from Inaccurate Annotations? DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 25

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Observation 0b5e71ea-9d53-4f18-a62f-f3a6681a55a9 · outbound

This paper cites A conditional multino- mial mixture model for superset label learning.Advances in neural information processing systems, 25, 2012.

Why Can Accurate Models Be Learned from Inaccurate Annotations? A conditional multino- mial mixture model for superset label learning.Advances in neural information processing systems, 25, 2012

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.535845Z

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Observation 07442517-f660-415b-8e47-ebd2afe2c39b · outbound

This paper cites Progressive identification of true labels 9 for partial-label learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Progressive identification of true labels 9 for partial-label learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.516145Z

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Observation dffcf7e2-66b6-48de-b899-0c677457d608 · outbound

This paper cites Deep graph matching for partial label learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Deep graph matching for partial label learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.496898Z

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Observation cb8965fd-daec-45d5-822f-49f19093e79c · outbound

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

Why Can Accurate Models Be Learned from Inaccurate Annotations? Normalized loss functions for deep learning with noisy labels

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.478988Z

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Observation 6aa543a3-6115-49ba-a754-bcb08d14409a · outbound

This paper cites SELF: Learning to Filter Noisy Labels with Self-Ensembling.

Why Can Accurate Models Be Learned from Inaccurate Annotations? SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 30

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Source-reported events for the cited work

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Observation 37d4e2a6-04f0-417d-9b7c-cd4d322b7a79 · outbound

This paper cites Classification with partial labels.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Classification with partial labels

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.460052Z

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Observation 6f7eed82-cc4e-4162-8c26-4e687e21a9ac · outbound

This paper cites an unresolved cited work.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Unresolved cited work

Reference 32

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Observation d0b065be-dce2-4cfd-885c-dc790fc87d10 · outbound

This paper cites Pytorch: An im- perative style, high-performance deep learning library.Ad- vances in neural information processing systems, 32, 2019.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Pytorch: An im- perative style, high-performance deep learning library.Ad- vances in neural information processing systems, 32, 2019

Reference 33

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Observation d2733b80-90fa-49f0-a184-20d18089eaf3 · outbound

This paper cites Making deep neural net- works robust to label noise: A loss correction approach.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Making deep neural net- works robust to label noise: A loss correction approach

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.414216Z

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Observation 3a42c891-ad15-4c59-8c1b-0e6b769c5052 · outbound

This paper cites The matrix cookbook.Technical University of Denmark, 7(15): 510, 2008.

Why Can Accurate Models Be Learned from Inaccurate Annotations? The matrix cookbook.Technical University of Denmark, 7(15): 510, 2008

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.396083Z

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Observation b5ff0a5e-decd-4407-b83a-91486e50bd5c · outbound

This paper cites Learning to reweight examples for robust deep learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Learning to reweight examples for robust deep learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.373599Z

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Observation 24bc533c-8776-49a7-b72e-14635b159117 · outbound

This paper cites Adaptive integration of par- tial label learning and negative learning for enhanced noisy label learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Adaptive integration of par- tial label learning and negative learning for enhanced noisy label learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.356603Z

Source-reported events for the cited work

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Observation 7385466a-0bf0-4cd6-8320-37c31bed46b7 · outbound

This paper cites Meta Transition Adaptation for Robust Deep Learning with Noisy Labels.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Meta Transition Adaptation for Robust Deep Learning with Noisy Labels

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:10:18.676760Z

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Observation 6cf4a771-7d87-4f51-a58b-ab88def96234 · outbound

This paper cites Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:10:18.530373Z

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Observation 5e7017d6-d26c-4b6c-a649-da1a4db925bd · outbound

This paper cites Partial label learning with a partner.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Partial label learning with a partner

Reference 40

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Observation 4c9903a9-91b3-4a7a-ada6-4ea3c7d9febf · outbound

This paper cites Unleashing the power of task-specific directions in parameter efficient fine-tuning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Unleashing the power of task-specific directions in parameter efficient fine-tuning

Reference 41

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Observation 5bcb93f3-275d-474a-9a07-cc0fff1afeab · outbound

This paper cites Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:14.101244Z

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Observation e20a1b73-ee20-4c60-8946-59c060423a0b · outbound

This paper cites See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition.

Why Can Accurate Models Be Learned from Inaccurate Annotations? See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition

Reference 43

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Observation 242f697a-c2f1-4cd5-9914-8bc6993584c0 · outbound

This paper cites Webly supervised fine-grained recognition: Benchmark datasets and an approach.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Webly supervised fine-grained recognition: Benchmark datasets and an approach

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.316908Z

Source-reported events for the cited work

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Observation 95d06472-07a8-4c46-a627-072559741288 · outbound

This paper cites Adaptive graph guided disambiguation for partial label learning.IEEE Trans.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Adaptive graph guided disambiguation for partial label learning.IEEE Trans

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.214579Z

Source-reported events for the cited work

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Observation 1ef74a38-1fb0-4bb2-9590-d808aa6976bf · outbound

This paper cites PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning

Reference 46

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Observation 61cd78d7-fc4d-4d22-99a6-ebfc14f5b23a · outbound

This paper cites Symmetric cross entropy for robust learn- ing with noisy labels.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Symmetric cross entropy for robust learn- ing with noisy labels

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.050917Z

Source-reported events for the cited work

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Observation 8d35a700-7792-412f-93e4-a30674bffdd0 · outbound

This paper cites Caltech-ucsd birds 200.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Caltech-ucsd birds 200

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.846633Z

Source-reported events for the cited work

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Observation 0b76922a-b431-44db-bca1-1f458294215d · outbound

This paper cites Revisiting consistency regularization for deep partial label learning.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Revisiting consistency regularization for deep partial label learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.788483Z

Source-reported events for the cited work

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Observation 02a6a8bf-b383-4a99-811f-c315784ff15b · outbound

This paper cites Instance-dependent partial label learning.Advances in Neu- ral Information Processing Systems, 34:27119–27130, 2021.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Instance-dependent partial label learning.Advances in Neu- ral Information Processing Systems, 34:27119–27130, 2021

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.771105Z

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Observation 9c6f422e-9854-4b6e-a59f-fce65457ac39 · outbound

This paper cites Probabilistic end-to-end noise cor- rection for learning with noisy labels.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Probabilistic end-to-end noise cor- rection for learning with noisy labels

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.754295Z

Source-reported events for the cited work

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Observation 429a1eb0-316c-418f-a109-f75409dd7ef9 · outbound

This paper cites Learning by associat- ing ambiguously labeled images.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Learning by associat- ing ambiguously labeled images

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.735232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9ddd18bc-37ec-4801-8b6d-fc4707c255c3 · outbound

This paper cites Partial label learning via feature-aware disambiguation.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Partial label learning via feature-aware disambiguation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.715985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 77efc49e-1397-415e-bb5c-efff1d8d26af · outbound

This paper cites Par- tial label learning via cost-guided retraining.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Par- tial label learning via cost-guided retraining

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.697291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bae8debe-05f8-4a96-9435-324ef4148496 · outbound

This paper cites Asymmetric loss functions for noise- tolerant learning: Theory and applications.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 45(7): 8094–8109, 2023.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Asymmetric loss functions for noise- tolerant learning: Theory and applications.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 45(7): 8094–8109, 2023

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.677241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e97c2109-2569-4df0-b1f9-0115c1750e5e · outbound

This paper cites A brief introduction to weakly supervised learning.National science review, 5(1):44–53, 2018.

Why Can Accurate Models Be Learned from Inaccurate Annotations? A brief introduction to weakly supervised learning.National science review, 5(1):44–53, 2018

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.557168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 08c613b1-5c9d-453e-9f66-fa429c94458d · outbound

This paper cites Goldberg.

Why Can Accurate Models Be Learned from Inaccurate Annotations? Goldberg

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.073468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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