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Paper Citation Record · LEDGER

When Source-Free Domain Adaptation Meets Learning with Noisy Labels

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2301.13381.

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

pith.paper-citation-record.v1
2301.13381 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:24:36.481851Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

20
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation fca863fd-d3c2-4b5a-be71-b93edbd22f8e · inbound

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation cites this paper.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation When Source-Free Domain Adaptation Meets Learning with Noisy Labels

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T12:24:36.481851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:24:36.481851Z digest=sha256:a0680f380ff9d6bfcb5cfb7ed3aed26ee48d69bbb1e238483e3f862eef18810a

Observation 4c93758b-7ffd-42fb-9857-7faecd08f2e9 · inbound

Interact3D: Compositional 3D Generation of Interactive Objects cites this paper.

Interact3D: Compositional 3D Generation of Interactive Objects When Source-Free Domain Adaptation Meets Learning with Noisy Labels

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-13T23:59:14.441938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:59:14.441938Z digest=sha256:74167cbd9654a7d8c291ded9fda940b15330fc612bc0b87cd608490a73878532

Observation af822753-4edd-4f5f-acd0-edca95475657 · inbound

Rethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model cites this paper.

Rethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model When Source-Free Domain Adaptation Meets Learning with Noisy Labels

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-08T18:28:58.248077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T18:26:24.377120Z digest=sha256:aba299ff86bd7c91ae8d2b57e3173f6bd0a0a442f41c37a81947c6856d5d451f

Observation b7c3c7f4-1951-4967-a678-446a793140fe · inbound

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation cites this paper.

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation When Source-Free Domain Adaptation Meets Learning with Noisy Labels

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T19:22:34.295195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T19:20:43.215270Z digest=sha256:01e81c62d7442144b569477abb44ba1befbede69bd79def042a57df336846f6f

Observation a47c832a-fd51-46aa-9a24-22f12ee87cab · inbound

Full spectrum Unlearnable Examples via Spectral Equalization cites this paper.

Full spectrum Unlearnable Examples via Spectral Equalization When Source-Free Domain Adaptation Meets Learning with Noisy Labels

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-04T12:59:52.826755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T05:36:18.782627Z digest=sha256:d068ea0bfbc0128e49174f3a16db9fdfa30d084113ccfda3730597d21007287b