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Paper Citation Record · LEDGER

Label Calibration in Source Free Domain Adaptation

As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2501.07072.

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

pith.paper-citation-record.v1
2501.07072 v1

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measured 36 of 36 reference resolution

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measured 36 of 36 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 6d1ac164-6fd0-4a49-bb03-34f4d4bf30b8 · outbound

This paper cites Continual evidential deep learning for out-of-distribution detection.

Label Calibration in Source Free Domain Adaptation Continual evidential deep learning for out-of-distribution detection

Reference 1

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Observation 6c8bdcc2-cdab-44be-ad60-48718e3fc646 · outbound

This paper cites Deep evidential regression.

Label Calibration in Source Free Domain Adaptation Deep evidential regression

Reference 2

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Observation 01b5cd8e-6163-4a82-a3de-31f6ae036e4f · outbound

This paper cites Deed: Deep evidential doctor.

Label Calibration in Source Free Domain Adaptation Deed: Deep evidential doctor

Reference 3

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Observation fc143ceb-a470-4977-8001-0d2a32880c38 · outbound

This paper cites Evidential deep learning for open set action recognition.

Label Calibration in Source Free Domain Adaptation Evidential deep learning for open set action recognition

Reference 4

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Observation 4f7edacc-2a26-4d3a-b4e7-9c11c400f4d2 · outbound

This paper cites Deep clustering for unsupervised learning of visual features.

Label Calibration in Source Free Domain Adaptation Deep clustering for unsupervised learning of visual features

Reference 5

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Observation 1d031c7a-3432-421c-a126-67b2249631ef · outbound

This paper cites Source-free domain adaptive fundus image segmentation with denoised pseudo- labeling.

Label Calibration in Source Free Domain Adaptation Source-free domain adaptive fundus image segmentation with denoised pseudo- labeling

Reference 6

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Observation 49692d40-1068-421f-a6c7-e449a8fde58a · outbound

This paper cites Think twice before selection: Federated evidential ac- tive learning for medical image analysis with domain shifts.

Label Calibration in Source Free Domain Adaptation Think twice before selection: Federated evidential ac- tive learning for medical image analysis with domain shifts

Reference 7

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Observation fac896ff-dd6e-4272-901a-1e731ea38a3e · outbound

This paper cites Uncertainty estimation by fisher information-based evidential deep learning.

Label Calibration in Source Free Domain Adaptation Uncertainty estimation by fisher information-based evidential deep learning

Reference 8

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Observation 68266ef7-d228-4e4f-8778-dc8113e5c4d7 · outbound

This paper cites Unsupervised domain adapta- tion via calibrating uncertainties.

Label Calibration in Source Free Domain Adaptation Unsupervised domain adapta- tion via calibrating uncertainties

Reference 9

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Observation e20efbf9-cf33-4e07-afbc-3fcea16cd6ef · outbound

This paper cites Deep residual learning for image recognition.

Label Calibration in Source Free Domain Adaptation Deep residual learning for image recognition

Reference 10

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Observation 0a280d8e-d0ed-464e-a81e-0a1f08c9b104 · outbound

This paper cites Cycada: Cycle-consistent adversar- ial domain adaptation.

Label Calibration in Source Free Domain Adaptation Cycada: Cycle-consistent adversar- ial domain adaptation

Reference 11

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Observation 1cea0e04-a83e-4dea-a1e1-953a2b8fe5c0 · outbound

This paper cites Kurmi, Venkatesh K.

Label Calibration in Source Free Domain Adaptation Kurmi, Venkatesh K

Reference 12

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Observation bd2751b5-1ba5-4452-a169-8892e1cc71fb · outbound

This paper cites Unsupervised domain adaptation based on the predictive uncertainty of mod- els.

Label Calibration in Source Free Domain Adaptation Unsupervised domain adaptation based on the predictive uncertainty of mod- els

Reference 13

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Observation 64fe4c4d-a0ec-4fc6-b8f5-6d0c6417e02a · outbound

This paper cites A comprehensive survey on source- free domain adaptation.

Label Calibration in Source Free Domain Adaptation A comprehensive survey on source- free domain adaptation

Reference 14

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Observation 08110409-95d0-4112-a4b8-5fb39ac08c7a · outbound

This paper cites Do we re- ally need to access the source data? source hypoth- esis transfer for unsupervised domain adaptation.

Label Calibration in Source Free Domain Adaptation Do we re- ally need to access the source data? source hypoth- esis transfer for unsupervised domain adaptation

Reference 15

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Observation 8454b610-7d3c-48c5-a920-beea9348ab66 · outbound

This paper cites Guiding pseudo-labels with uncertainty estima- tion for source-free unsupervised domain adaptation.

Label Calibration in Source Free Domain Adaptation Guiding pseudo-labels with uncertainty estima- tion for source-free unsupervised domain adaptation

Reference 16

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Observation 3356dd60-0b2a-4e59-ae06-5976508740c6 · outbound

This paper cites Deep unsupervised domain adaptation: A review of recent advances and perspectives.

Label Calibration in Source Free Domain Adaptation Deep unsupervised domain adaptation: A review of recent advances and perspectives

Reference 17

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Observation 5b571aec-aa9f-498f-be0b-eec73732a25c · outbound

This paper cites Uncertainty-aware source-free do- main adaptive semantic segmentation.

Label Calibration in Source Free Domain Adaptation Uncertainty-aware source-free do- main adaptive semantic segmentation

Reference 18

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Observation 3b878a9d-3551-438e-8bf0-20a1938f1554 · outbound

This paper cites Comparison of parameter tuning strategies for team orienteering problem (top) solved with gurobi.

Label Calibration in Source Free Domain Adaptation Comparison of parameter tuning strategies for team orienteering problem (top) solved with gurobi

Reference 19

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Observation 79cf68e1-e4f6-4920-9443-ad37697e31df · outbound

This paper cites Learn to accumulate evidence from all training samples: theory and prac- tice.

Label Calibration in Source Free Domain Adaptation Learn to accumulate evidence from all training samples: theory and prac- tice

Reference 20

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Observation 3f22768d-5bd8-4af1-b04f-f5096787acdd · outbound

This paper cites Active learn- ing for object detection with evidential deep learn- ing and hierarchical uncertainty aggregation.

Label Calibration in Source Free Domain Adaptation Active learn- ing for object detection with evidential deep learn- ing and hierarchical uncertainty aggregation

Reference 21

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Observation ebd7f5eb-a7c4-4fec-b6e3-42c873d5032c · outbound

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

Label Calibration in Source Free Domain Adaptation Moment matching for multi-source domain adaptation

Reference 22

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Observation d58cd85a-b640-47b6-9e30-5555554f0fdc · outbound

This paper cites Prior- guided source-free domain adaptation for human pose estimation.

Label Calibration in Source Free Domain Adaptation Prior- guided source-free domain adaptation for human pose estimation

Reference 23

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This paper cites Uncertainty-guided source-free domain adaptation.

Label Calibration in Source Free Domain Adaptation Uncertainty-guided source-free domain adaptation

Reference 24

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This paper cites Adapting visual category models to new do- mains.

Label Calibration in Source Free Domain Adaptation Adapting visual category models to new do- mains

Reference 25

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This paper cites Evidential deep learning to quantify classification un- certainty.

Label Calibration in Source Free Domain Adaptation Evidential deep learning to quantify classification un- certainty

Reference 26

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Observation f67876b7-8e2a-4c58-96e3-fa7ecd870c78 · outbound

This paper cites Prior knowl- edge guided unsupervised domain adaptation.

Label Calibration in Source Free Domain Adaptation Prior knowl- edge guided unsupervised domain adaptation

Reference 27

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This paper cites Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation.

Label Calibration in Source Free Domain Adaptation Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation

Reference 28

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Label Calibration in Source Free Domain Adaptation Deep hashing network for unsupervised domain adaptation

Reference 29

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This paper cites Upl-sfda: Uncertainty- aware pseudo label guided source-free domain adapta- tion for medical image segmentation.

Label Calibration in Source Free Domain Adaptation Upl-sfda: Uncertainty- aware pseudo label guided source-free domain adapta- tion for medical image segmentation

Reference 30

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Observation 7fee0b3b-460a-4a48-8038-c6fd1d37e845 · outbound

This paper cites Dirichlet-based Uncertainty Calibration for Active Domain Adaptation.

Label Calibration in Source Free Domain Adaptation Dirichlet-based Uncertainty Calibration for Active Domain Adaptation

Reference 31

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Observation 7fc511c1-fcb2-4023-b340-5f0ca6b6908e · outbound

This paper cites Deep evidential fusion network for medical im- age classification.

Label Calibration in Source Free Domain Adaptation Deep evidential fusion network for medical im- age classification

Reference 32

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Observation 6b693919-6566-43fd-a531-7eb3457b6ba9 · outbound

This paper cites Transformer-Based Source-Free Domain Adaptation.

Label Calibration in Source Free Domain Adaptation Transformer-Based Source-Free Domain Adaptation

Reference 33

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This paper cites Dirichlet-based prediction calibra- tion for learning with noisy labels.

Label Calibration in Source Free Domain Adaptation Dirichlet-based prediction calibra- tion for learning with noisy labels

Reference 34

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Label Calibration in Source Free Domain Adaptation Unresolved cited work

Reference 235

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Observation f84a10ec-1504-4409-b6a7-310809f3d508 · outbound

This paper cites 2, 3, 5, 6, 7, 8.

Label Calibration in Source Free Domain Adaptation 2, 3, 5, 6, 7, 8

Reference 655

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:53:23.283865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T20:53:23.105542Z digest=sha256:b2ac0953cc219d9096181ed8bd7b058beefe873e6c7cc9327613c336fefd6d3e

Pith citing papers

No inbound Pith citation observations are available.