Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:10:16.486395Z
Paper Citation Record · LEDGER
Why Can Accurate Models Be Learned from Inaccurate Annotations?
As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2505.16159.
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 2026-08-15T06:32:42.880941+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
57 of 57 outbound references displayed
- verified exact3
- verified fuzzy44
- unresolved10
- parse uncertain0
- malformed identifier0
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External citation measurements
No source-named external measurement is stored.
Outbound references
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e481a62a-90df-411b-9b36-37bc56481044 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:10.561350Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:10.867002Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:12.109904Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:12.600687Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T15:10:20.442384Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:12.961840Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:10:20.339081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:14.023562Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:14.195761Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:10:14.471893Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.