Paper Citation Record · LEDGER

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning

As of 19 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2508.20381.

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

pith.paper-citation-record.v1
2508.20381 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:11:46.375626Z

measured 35 of 35 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy27
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9d37942-7f38-4399-8e08-10bfcfb7c0e1 · outbound

This paper cites Cdul: Clip-driven unsupervised learning for multi-label image classification.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Cdul: Clip-driven unsupervised learning for multi-label image classification

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.924848Z

Source-reported events for the cited work

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Observation 6510e78c-ab2d-4b65-ab12-2fce6329ba63 · outbound

This paper cites Boosting single positive multi-label classifica- tion with generalized robust loss.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Boosting single positive multi-label classifica- tion with generalized robust loss

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.845857Z

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Observation 849c6df0-f6ec-4628-8a90-39f46e580ede · outbound

This paper cites Multi-label image recognition with graph convolu- tional networks.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Multi-label image recognition with graph convolu- tional networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.789874Z

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Observation 67890e31-0a28-4570-9fcd-3a75e5d1d0e2 · outbound

This paper cites Nus-wide: a real-world web im- age database from national university of singapore.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Nus-wide: a real-world web im- age database from national university of singapore

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.753635Z

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Observation cf4168c3-2453-48cc-9383-7424ede04eca · outbound

This paper cites Multi-label learning from single positive labels.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Multi-label learning from single positive labels

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.710497Z

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Observation 81fd2ecd-3450-4e83-80ef-d6b7ed67a845 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Imagenet: A large-scale hierarchical image database

Reference 6

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Source-reported events for the cited work

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Observation 8bce1bcc-f6cd-4344-b941-0bf2e1682e12 · outbound

This paper cites Scalable multi-label annotation.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Scalable multi-label annotation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.650627Z

Source-reported events for the cited work

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Observation 8d4a3bca-9b98-4fbf-8daa-4b629a12c60a · outbound

This paper cites Explor- ing structured semantic prior for multi label recognition with incomplete labels.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Explor- ing structured semantic prior for multi label recognition with incomplete labels

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.620666Z

Source-reported events for the cited work

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Observation 0a52bf85-cf07-4378-b7f7-9c67f43256c4 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.576529Z

Source-reported events for the cited work

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Observation 1ab298e0-c8bc-4de2-b3aa-b7ebe8057861 · outbound

This paper cites The pascal visual object classes (voc) challenge.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning The pascal visual object classes (voc) challenge

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.545059Z

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.

Observation 53126c3a-b66c-4448-9793-a9b68011e657 · outbound

This paper cites Deep residual learning for image recognition.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Deep residual learning for image recognition

Reference 11

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Observation 98e18584-629f-4bb2-bbe0-220329686f40 · outbound

This paper cites NEFTune: Noisy Embeddings Improve Instruction Finetuning.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning NEFTune: Noisy Embeddings Improve Instruction Finetuning

Reference 12

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Observation 4b2de954-e703-4913-b94c-b42fb4502fb7 · outbound

This paper cites Large loss matters in weakly supervised multi- label classification.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Large loss matters in weakly supervised multi- label classification

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.454816Z

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.

Observation bff04cfd-d6a7-405b-91be-2c007fd018f0 · outbound

This paper cites Bridging the gap between model explanations in partially annotated multi- label classification.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Bridging the gap between model explanations in partially annotated multi- label classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.408153Z

Source-reported events for the cited work

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Observation 0bb31f09-1e87-4895-92cf-f5b73353c673 · outbound

This paper cites Kingma and Jimmy Ba.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Kingma and Jimmy Ba

Reference 15

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Observation 9aec6396-5654-4a80-aabd-3f6330e6c74a · outbound

This paper cites Kipf and Max Welling.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Kipf and Max Welling

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.357258Z

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.

Observation 71a1fa05-f47e-4082-90e7-3e29a4f2258f · outbound

This paper cites Robust optimization as data augmentation for large-scale graphs.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Robust optimization as data augmentation for large-scale graphs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.302279Z

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.

Observation 99f33e87-56bd-4e30-9b9c-502f7838bf19 · outbound

This paper cites Microsoft coco: Common objects in context.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Microsoft coco: Common objects in context

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.263917Z

Source-reported events for the cited work

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Observation 02d346b8-3539-41eb-b11d-0b8e9878a6a8 · outbound

This paper cites Revisiting pseudo-label for single-positive multi-label learning.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Revisiting pseudo-label for single-positive multi-label learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.228122Z

Source-reported events for the cited work

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Observation 87fc7cf3-5d2b-43e8-a439-2363b5106503 · outbound

This paper cites The emerging trends of multi-label learning.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning The emerging trends of multi-label learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.191795Z

Source-reported events for the cited work

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Observation e9e6b80e-47f8-49a4-9119-fb1265a2364e · outbound

This paper cites A convnet for the 2020s.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning A convnet for the 2020s

Reference 21

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Observation 9d2253d5-58a6-4883-8896-e348a261d450 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Learning transferable visual models from natural language supervi- sion

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.114739Z

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Observation c023abca-4638-450d-81b4-e0b1dda7068e · outbound

This paper cites Multi-label classifica- tion with missing labels using label correlation and robust structural learning.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Multi-label classifica- tion with missing labels using label correlation and robust structural learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.053722Z

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

Observation 008aa2af-3a8c-471b-bd08-f383af537684 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning The caltech-ucsd birds-200-2011 dataset

Reference 24

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Observation 173374e0-b30f-46bf-ae76-ecb5238ff469 · outbound

This paper cites Multi-label learning with missing labels.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Multi-label learning with missing labels

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.976062Z

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.

Observation 8c576c15-480f-46d2-8e09-ffe712f0820a · outbound

This paper cites Ml-mg: Multi-label learning with missing labels using a mixed graph.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Ml-mg: Multi-label learning with missing labels using a mixed graph

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.947123Z

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.

Observation d2af2b57-8f86-4eaa-aa45-6987b98b84a2 · outbound

This paper cites Vision-language pseudo- labels for single-positive multi-label learning.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Vision-language pseudo- labels for single-positive multi-label learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.908926Z

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.

Observation 526db189-4772-4b00-9d19-4d2666578931 · outbound

This paper cites One positive label is sufficient: Single-positive multi- label learning with label enhancement.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning One positive label is sufficient: Single-positive multi- label learning with label enhancement

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.870237Z

Source-reported events for the cited work

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Observation 00cc021b-0331-47b8-8b92-a76b92ccba75 · outbound

This paper cites Large-scale multi-label learning with missing la- bels.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Large-scale multi-label learning with missing la- bels

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.824748Z

Source-reported events for the cited work

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Observation 0cbda509-404b-495c-9d42-f74aa8b4d155 · outbound

This paper cites Vision-language models for vision tasks: A survey.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Vision-language models for vision tasks: A survey

Reference 30

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Observation cd6a9ffa-317b-4a27-a95a-4ea661c56093 · outbound

This paper cites A review on multi-label learning algorithms.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning A review on multi-label learning algorithms

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.740303Z

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.

Observation 6bfb1693-cac9-44c7-b1d1-45a4b0e1e0db · outbound

This paper cites Learning in im- perfect environment: Multi-label classification with long- tailed distribution and partial labels.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Learning in im- perfect environment: Multi-label classification with long- tailed distribution and partial labels

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.682060Z

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.

Observation d5f5bb65-84f0-4a2e-a2dd-b0de5391555e · outbound

This paper cites Simple and Robust Loss Design for Multi-Label Learning with Missing Labels.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Reference 33

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Source-reported events for the cited work

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Observation 7b1758e6-3537-4972-8c04-55fa1483cfd9 · outbound

This paper cites Acknowledging the unknown for multi-label learning with single positive labels.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Acknowledging the unknown for multi-label learning with single positive labels

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.639728Z

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.

Observation 7d58e54b-7931-40b6-ba69-947fd0feacbb · outbound

This paper cites Freelb: Enhanced adversarial training for natural language understanding.

More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning Freelb: Enhanced adversarial training for natural language understanding

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:46.587923Z

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.

Pith citing papers

No inbound Pith citation observations are available.