Paper Citation Record · LEDGER

Weakly-Supervised Contrastive Learning for Imprecise Class Labels

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2505.22028.

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

pith.paper-citation-record.v1
2505.22028 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:24:25.710526Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

22 of 22 outbound references displayed

verified exact0
verified fuzzy13
unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 482e2ffb-ec2f-47a8-92ac-dd31b9a0d260 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:26.181081Z

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

Observation 200692f6-2ab9-4492-bf3b-6a73f10c6677 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:27.342049Z

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Observation 1ab1596d-41b8-4644-91a3-9c77e6219ffc · outbound

This paper cites Each runs has been repeated 3 times with different randomly-generated noise and we report the best accuracy.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Each runs has been repeated 3 times with different randomly-generated noise and we report the best accuracy

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:27.199177Z

Source-reported events for the cited work

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Observation c8ebdfa8-c44f-412d-b527-6131a0adac54 · outbound

This paper cites Lemma C.15.LetFbe a hypothesis class of feature extractors fromXtoR d with∥F ∥∞ =κ.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Lemma C.15.LetFbe a hypothesis class of feature extractors fromXtoR d with∥F ∥∞ =κ

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:28.122978Z

Source-reported events for the cited work

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Observation 93c74fb2-5fe5-4fb8-8529-bbe86ed1185c · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:27.966870Z

Source-reported events for the cited work

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Observation 08042477-d624-43e2-8247-27a08288bc4c · outbound

This paper cites Each runs has been repeated 3 times with different randomly- generated partial labels and we report the mean and std values of last 5 epochs.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Each runs has been repeated 3 times with different randomly- generated partial labels and we report the mean and std values of last 5 epochs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:26.793518Z

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 3cb710a3-226d-4561-891c-999b051069cc · outbound

This paper cites Table 10.Ablation studies of our proposed algorithm on CIFAR-100 with different ratio of noisy label and partial label.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Table 10.Ablation studies of our proposed algorithm on CIFAR-100 with different ratio of noisy label and partial label

Reference 10

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T13:24:26.671794Z

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 27d83083-6c17-4db8-975b-5878bd17a876 · outbound

This paper cites The second way to construct S is the same as the method used in our main paper.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels The second way to construct S is the same as the method used in our main paper

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:26.315243Z

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 8da8fb0d-cbcc-43a2-b366-a1d06c5abc13 · outbound

This paper cites We train our model for 500 epochs and set the initial learning rate be 0.1 and adjust it by Cosine scheduler on both four fine-grained datasets.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels We train our model for 500 epochs and set the initial learning rate be 0.1 and adjust it by Cosine scheduler on both four fine-grained datasets

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:27.065280Z

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 933ec88d-0478-40a7-941e-be2c3a46c5e5 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:26.947702Z

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 3bcd2c65-45f1-4c68-91d9-1448d9166b67 · outbound

This paper cites They obtain the estimated noise matrix by searching for the matrix that minimizes a specific metric within the matrix family that can linearly represent all noise posteriors.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels They obtain the estimated noise matrix by searching for the matrix that minimizes a specific metric within the matrix family that can linearly represent all noise posteriors

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:26.386332Z

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 74bf043d-a538-4ab3-a769-cd16bd5e1621 · outbound

This paper cites The second way to construct S is the same as the method used in our main paper.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels The second way to construct S is the same as the method used in our main paper

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:26.045516Z

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 d6fb8ec3-72ea-46d5-a4ec-c88f28a16c48 · outbound

This paper cites We report the mean and std values of last 5 epochs.Env.,Sap.denote the environment information and sample information, respectively.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels We report the mean and std values of last 5 epochs.Env.,Sap.denote the environment information and sample information, respectively

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:25.905619Z

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 b753e9c3-bd2c-4e0b-8a99-fabc4b293416 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:28.255330Z

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 92d4faaf-87b0-412f-ace1-c43edd299952 · outbound

This paper cites Catherine Wah, Steve Branson, Peter Welinder, Pietro Per- ona, and Serge J.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Catherine Wah, Steve Branson, Peter Welinder, Pietro Per- ona, and Serge J

Reference 200

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:28.514831Z

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 43c3d377-9b48-458a-937c-a2b0b3d2d581 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Representation Learning with Contrastive Predictive Coding

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:24.229999Z

Source-reported events for the cited work

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Observation d96e4a13-6f44-41d7-a956-c56056ab96c7 · outbound

This paper cites In recent years, it is the latter (Li et al., 2021; Zhang et al., 2021b; Lin et al.,.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels In recent years, it is the latter (Li et al., 2021; Zhang et al., 2021b; Lin et al.,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:26.508755Z

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 229ceda0-5caf-4551-ac24-145d5308a731 · outbound

This paper cites Wen et al.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Wen et al

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:27.795994Z

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 779eeedd-3a38-4528-8a86-d023be9dc00e · outbound

This paper cites 2021 in this section.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels 2021 in this section

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:28.396686Z

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 a7c87136-e074-4bb0-9ca6-d470e3df7c3f · outbound

This paper cites The former facilitates the formation of well-structured clusters, which in turn enables prototype learning to acquire prototype representations.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels The former facilitates the formation of well-structured clusters, which in turn enables prototype learning to acquire prototype representations

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:27.490046Z

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 c6a4b11c-1caf-48ea-b1ae-64b53100a4d7 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Fine-Grained Visual Classification of Aircraft

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:24.171646Z

Source-reported events for the cited work

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Observation 02545873-8216-41b5-9a20-d7f5709abb9d · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Contrastive Learning for Imprecise Class Labels Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:27.631307Z

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.

Pith citing papers

No inbound Pith citation observations are available.