Paper Citation Record · LEDGER

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.04732.

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

pith.paper-citation-record.v1
2106.04732 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:10:22.388630Z

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

22
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 70669a0b-9514-4047-9386-a244e5cdca4c · inbound

SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning cites this paper.

SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T12:10:22.388630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43b1f9a0-6faf-4092-a8a2-6ada5a114c9e · inbound

Harmonizing and Merging Source Models for CLIP-based Domain Generalization cites this paper.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.820501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac615e13-0d64-4481-bc21-69c872287a99 · inbound

Learning from Limited and Imperfect Data cites this paper.

Learning from Limited and Imperfect Data AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.902108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4220eb6d-c6df-4edb-b327-912108478ba0 · inbound

MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder cites this paper.

MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T10:15:31.865753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bed49280-f09d-4405-8d46-5d93a2977f85 · inbound

$\mu$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM cites this paper.

$\mu$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T14:49:31.543901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c40438ef-b832-429c-9379-48e43e987f21 · inbound

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition cites this paper.

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:47:08.972875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.