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Paper Citation Record · LEDGER

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation

As of 14 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2411.13152.

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

pith.paper-citation-record.v1
2411.13152 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:49:53.292978Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

28 of 28 outbound references displayed

  • verified exact0
  • verified fuzzy23
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d938653c-487c-443b-9647-c9144562822a · outbound

This paper cites AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 1

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Source-reported events for the cited work

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Observation 37d4cf6d-b0c0-4e71-82ba-c105a1346080 · outbound

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

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Imagenet: A large-scale hierarchical image database

Reference 2

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no resolver link, observed 2026-08-12T16:49:53.155635Z

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Observation 63e7507d-cb3a-4f8c-8624-a88a59d6f541 · outbound

This paper cites Domain-adversarial training of neural networks.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Domain-adversarial training of neural networks

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 4f61ede5-7598-47f7-9db9-c7f75ea32f2a · outbound

This paper cites Semi-supervised learning by entropy minimization.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Semi-supervised learning by entropy minimization

Reference 4

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation f78aa5c3-d3d4-4022-969a-5578aaa711fc · outbound

This paper cites Classification-aware semi-supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Classification-aware semi-supervised domain adaptation

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 384c3f11-0b17-414e-bb81-65f6dcfe394e · outbound

This paper cites Deep residual learning for image recognition.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Deep residual learning for image recognition

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e869f4a0-f675-4dfc-a8e1-fca476fe380b · outbound

This paper cites Bidirectional adversarial training for semi-supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Bidirectional adversarial training for semi-supervised domain adaptation

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 7b96d37c-6052-43e4-a7bb-ead1ba1b22d3 · outbound

This paper cites Attract, perturb, and explore: Learning a feature alignment network for semi- supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Attract, perturb, and explore: Learning a feature alignment network for semi- supervised domain adaptation

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 9fae7864-177b-4c4f-9f37-2293b249beff · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Semi-Supervised Classification with Graph Convolutional Networks

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f8c2dc57-9e1a-47e8-a515-de31c89f3141 · outbound

This paper cites Online meta-learning for multi-source and semi-supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Online meta-learning for multi-source and semi-supervised domain adaptation

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation bfbdc69e-72c9-4d84-9fed-c42e6eaa44ff · outbound

This paper cites Cross- domain adaptive clustering for semi-supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Cross- domain adaptive clustering for semi-supervised domain adaptation

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 159dd769-e3ea-40cb-9550-4501c180020a · outbound

This paper cites Semi-supervised domain adaptation by covariance matching.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Semi-supervised domain adaptation by covariance matching

Reference 12

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.214365Z digest=sha256:7a0e25ad0ea199b0d2d6c7aa6f34814f56acb3a353e73e5da5e4e26a04133576

Observation b06f0cfd-728f-4bee-acf0-970f5fdbb73d · outbound

This paper cites Adjustment and alignment for unbiased open set domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Adjustment and alignment for unbiased open set domain adaptation

Reference 13

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation f2c18a4d-af34-4786-8e9a-9b4d97e8bbe2 · outbound

This paper cites Semi-supervised domain adaptation for dependency parsing.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Semi-supervised domain adaptation for dependency parsing

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation fdfc12af-fd73-47a5-9813-f47f99cec9ea · outbound

This paper cites Guiding pseudo-labels with uncertainty estimation for source-free un- supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Guiding pseudo-labels with uncertainty estimation for source-free un- supervised domain adaptation

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-12T16:49:53.639459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 55cd2128-eb31-4a6e-b149-093adbee62c2 · outbound

This paper cites Gcan: Graph convolutional adversarial network for unsupervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Gcan: Graph convolutional adversarial network for unsupervised domain adaptation

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation e1f7627e-8607-41d4-82d9-c7f4289a3584 · outbound

This paper cites Unsupervised domain adap- tation of object detectors: A survey.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Unsupervised domain adap- tation of object detectors: A survey

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 96fbb337-eb79-4d1e-84ff-951732f61fd7 · outbound

This paper cites Moment matching for multi-source domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Moment matching for multi-source domain adaptation

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 0ce272f6-e424-4b44-aa51-279c94667631 · outbound

This paper cites Mul- timatch: Multi-task learning for semi-supervised domain generalization.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Mul- timatch: Multi-task learning for semi-supervised domain generalization

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.246931Z digest=sha256:56828e5663ab6c58c1a8d3ead3285b6e30bd7b25995a9fdcb095d296aad89b10

Observation 46bbef1c-43eb-4e59-918e-099302938f28 · outbound

This paper cites Semi-supervised domain adaptation via minimax entropy.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Semi-supervised domain adaptation via minimax entropy

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.251256Z digest=sha256:ec315a4db81b0d46a31bae9b32f6d38edcd213514f146c4550a00587a69575b2

Observation 993d3ebc-07a3-4762-b637-a67c4339ba03 · outbound

This paper cites Clda: Contrastive learning for semi-supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Clda: Contrastive learning for semi-supervised domain adaptation

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.256095Z digest=sha256:2eb68ff1cce7f81823118ec2ab6b8b5b342d363d04a822c3db7f3852aa7b574d

Observation 3c3c36f3-523e-4ac9-bd5d-227ddfcbb8ef · outbound

This paper cites Rethinking domain generalization for face anti- spoofing: Separability and alignment.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Rethinking domain generalization for face anti- spoofing: Separability and alignment

Reference 22

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.262298Z digest=sha256:0ec3d0ab95997ef6b9163e45963dbdf3cf8f68063bce8b8d00acef25f2d50f91

Observation f821dd68-f2b6-4411-a4c0-5e1be1d95f89 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Deep hashing network for unsupervised domain adaptation

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-12T16:49:53.498679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.268568Z digest=sha256:4a92e0b8d116bf6096995a2d59d6fb22d49cb318ab8b2ccac2d60cd4893260b9

Observation 3f1d75cc-b3b6-4fc7-b651-2e5ca71cd230 · outbound

This paper cites Ssda3d: Semi-supervised domain adaptation for 3d object detection from point cloud.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Ssda3d: Semi-supervised domain adaptation for 3d object detection from point cloud

Reference 24

Resolution
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raw_fallback, observed 2026-08-12T16:49:53.479419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.272606Z digest=sha256:38800d7e39f94cfc40ed93786572b36bce3f48c7c6f735231d204ac0eabe5e37

Observation 762533ee-7166-4c05-b2b1-4b51baaab6a5 · outbound

This paper cites Multi-level Consistency Learning for Semi-supervised Domain Adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Multi-level Consistency Learning for Semi-supervised Domain Adaptation

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:49:53.276705Z digest=sha256:4bc5f96ebbe1360b721a05ba61abd0aaa9cf02cd2da20e9df86f12a641b79fe4

Observation 00eb3560-0aab-4581-9a82-bb4c7e60e6a8 · outbound

This paper cites Deep co-training with task decomposition for semi- supervised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Deep co-training with task decomposition for semi- supervised domain adaptation

Reference 26

Resolution
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raw_fallback, observed 2026-08-12T16:49:53.459275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.280939Z digest=sha256:05c0dc409a33f9181d354ee424286dd0f049f43c3d80202e9240cee296d79362

Observation 5d825b61-9e96-4955-9dfb-ef2dee30cd32 · outbound

This paper cites Semi-supervised domain adaptation with source label adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Semi-supervised domain adaptation with source label adaptation

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:49:53.288561Z digest=sha256:752ef0bb0dc225ed0829a4f1d2f605a2e765b32589809d7482736c0950e0945d

Observation 09840df5-24f6-49a6-926e-752af1e7aa70 · outbound

This paper cites Make the u in uda matter: Invariant consistency learning for unsuper- vised domain adaptation.

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation Make the u in uda matter: Invariant consistency learning for unsuper- vised domain adaptation

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:53.420357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Pith citing papers

No inbound Pith citation observations are available.