Paper Citation Record · LEDGER

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain

As of 10 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2606.00558.

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

pith.paper-citation-record.v1
2606.00558 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T19:29:34.901868Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

16 of 16 outbound references displayed

verified exact3
verified fuzzy0
unresolved12
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4933e845-3a11-4875-8bed-b06e1e290f60 · outbound

This paper cites A Recent Survey of Heterogeneous Transfer Learning.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain A Recent Survey of Heterogeneous Transfer Learning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:32:34.900396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 512ca432-4cd1-477a-a606-96fb850dee62 · outbound

This paper cites Transferability in Deep Learning: A Survey.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Transferability in Deep Learning: A Survey

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:32:34.903210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4d620dfd-bcf4-4cfd-a8f2-a02d02c02b71 · outbound

This paper cites Towards understanding why fixmatch generalizes better than super- vised learning.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Towards understanding why fixmatch generalizes better than super- vised learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a61e3218-153b-4f59-b869-144519718953 · outbound

This paper cites The Caltech-UCSD Birds-200-2011 dataset.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain The Caltech-UCSD Birds-200-2011 dataset

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c03aa02-c16a-4f34-975f-375873716cac · outbound

This paper cites Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:32:34.905939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7374e68e-ddd2-48c9-ac54-768dbe8bfbd5 · outbound

This paper cites • Appendix A: Related Work.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain • Appendix A: Related Work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66d9a843-3412-4ffa-92cd-115b4c477291 · outbound

This paper cites For instance, several studies (Long et al., 2013; 2015; 2019; Yao et al., 2019; 2020; Cheng et al.,.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain For instance, several studies (Long et al., 2013; 2015; 2019; Yao et al., 2019; 2020; Cheng et al.,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20c01fc7-a0fa-4d36-87b7-a909c41f2f52 · outbound

This paper cites Another line of research (Ganin et al., 2016; Long et al., 2018; Liu et al., 2021; Gao et al., 2021; Shi & Liu, 2023; Meegahapola et al., 2024; Xu et al.,.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Another line of research (Ganin et al., 2016; Long et al., 2018; Liu et al., 2021; Gao et al., 2021; Shi & Liu, 2023; Meegahapola et al., 2024; Xu et al.,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0697ee42-24d7-422b-96bc-2917810654d3 · outbound

This paper cites Furthermore, several studies (Gu et al., 2022; Bai et al., 2024; Liu et al., 2024; Ren et al.,.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Furthermore, several studies (Gu et al., 2022; Bai et al., 2024; Liu et al., 2024; Ren et al.,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9418bbc-ad5d-4e30-b7c1-e74a185e91e6 · outbound

This paper cites (2026) further study transferability estimation before domain adaptation.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain (2026) further study transferability estimation before domain adaptation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c15222bf-dc8e-483b-bde4-9afbfd74cebd · outbound

This paper cites Another line of research (Grandvalet & Bengio, 2004; Cui et al., 2020; Zhang et al.,.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Another line of research (Grandvalet & Bengio, 2004; Cui et al., 2020; Zhang et al.,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30d98189-e210-4a48-85ec-e6a110f3d885 · outbound

This paper cites An example is LERM (Zhang et al., 2024), which utilizes class-specific label-encodings to guide the learning of unlabeled samples.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain An example is LERM (Zhang et al., 2024), which utilizes class-specific label-encodings to guide the learning of unlabeled samples

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edff5ee9-1b3c-47c7-9709-d2c126f478d7 · outbound

This paper cites In the target domain, we apply weak and strong augmentation techniques (Cubuk et al., 2020).

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain In the target domain, we apply weak and strong augmentation techniques (Cubuk et al., 2020)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc353a02-200f-4797-b4dd-8096941e67ec · outbound

This paper cites The model is trained using Adam with a batch size of 16 and a learning rate of 2e-5.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain The model is trained using Adam with a batch size of 16 and a learning rate of 2e-5

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa747983-f745-4f67-b0ff-f080e731db0a · outbound

This paper cites Table 10.Detailed parameter configurations used in this paper.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Table 10.Detailed parameter configurations used in this paper

Reference 15

Resolution: malformed identifier; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation decda0e5-783f-4549-b1bd-bc39985bfc69 · outbound

This paper cites CL applies a contrastive loss between weakly-augmented and strongly-augmented unlabeled target samples to encourage consistent representations.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain CL applies a contrastive loss between weakly-augmented and strongly-augmented unlabeled target samples to encourage consistent representations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T19:29:34.901868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.