Paper Citation Record · LEDGER

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation

As of 8 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2505.24216.

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

pith.paper-citation-record.v1
2505.24216 v1

Coverage vector

measured 49 of 49 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-08-07T12:37:25.553078Z

Reference resolution

49 of 49 outbound references displayed

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Outbound references

Observation 138cd803-d9c4-4d37-8c44-f50db9a54d7d · outbound

This paper cites However, domain shifts can degrade network perfor- mance considerably [1].

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation However, domain shifts can degrade network perfor- mance considerably [1]

Reference 1

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Observation 445e2ad3-54e8-4f00-a33a-858610c63eac · outbound

This paper cites Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation

Reference 2

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Unresolved cited work

Reference 3

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Unresolved cited work

Reference 4

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This paper cites For the single-target setting, our method achieves an average accuracy of86.7%, outperforming the baseline AdaContrast [6] by a significant margin of7.3%.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation For the single-target setting, our method achieves an average accuracy of86.7%, outperforming the baseline AdaContrast [6] by a significant margin of7.3%

Reference 5

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Unresolved cited work

Reference 6

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This paper cites Learning transferable features with deep adaptation networks,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Learning transferable features with deep adaptation networks,

Reference 7

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This paper cites Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods across three benchmark datasets.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods across three benchmark datasets

Reference 8

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Unresolved cited work

Reference 9

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Observation 449b63c5-9183-46b8-b96c-4e75ad4b8c3a · outbound

This paper cites Adver- sarial discriminative domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Adver- sarial discriminative domain adaptation,

Reference 10

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Observation 02705ec4-c4db-482f-a52d-45d0efeae0ee · outbound

This paper cites Unsupervised domain adapta- tion by backpropagation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Unsupervised domain adapta- tion by backpropagation,

Reference 11

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This paper cites Conditional adversarial domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Conditional adversarial domain adaptation,

Reference 12

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This paper cites Unsupervised domain adaptation us- ing feature aligned maximum classifier discrepancy,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Unsupervised domain adaptation us- ing feature aligned maximum classifier discrepancy,

Reference 13

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Observation 0550c642-b1aa-41d0-9daa-3fd6c3950ba4 · outbound

This paper cites Con- trastive test-time adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Con- trastive test-time adaptation,

Reference 14

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This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,

Reference 15

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This paper cites Min- imum class confusion for versatile domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Min- imum class confusion for versatile domain adaptation,

Reference 16

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This paper cites Bridging theory and algorithm for domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Bridging theory and algorithm for domain adaptation,

Reference 17

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This paper cites Central moment discrep- ancy for domain-invariant representation learning,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Central moment discrep- ancy for domain-invariant representation learning,

Reference 18

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This paper cites While this method im- proved the robustness of pseudo-labels, it incurred a signif- icant computational cost due to the need for multiple aug- mentations and predictions.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation While this method im- proved the robustness of pseudo-labels, it incurred a signif- icant computational cost due to the need for multiple aug- mentations and predictions

Reference 19

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Observation 14944f2d-5bfb-4485-845d-fddf2fedfc82 · outbound

This paper cites CyCADA: Cycle- consistent adversarial domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation CyCADA: Cycle- consistent adversarial domain adaptation,

Reference 20

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This paper cites Tent: Fully test-time adaptation by entropy minimiza- tion,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Tent: Fully test-time adaptation by entropy minimiza- tion,

Reference 21

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This paper cites Casting a bait for offline and online source-free do- main adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Casting a bait for offline and online source-free do- main adaptation,

Reference 22

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This paper cites Universal source-free domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Universal source-free domain adaptation,

Reference 23

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This paper cites Fixmatch: Simplifying semi-supervised learning with con- sistency and confidence,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Fixmatch: Simplifying semi-supervised learning with con- sistency and confidence,

Reference 24

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This paper cites Freematch: Self-adaptive thresh- olding for semi-supervised learning,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Freematch: Self-adaptive thresh- olding for semi-supervised learning,

Reference 25

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This paper cites Learn- ing to reweight examples for robust deep learning,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Learn- ing to reweight examples for robust deep learning,

Reference 26

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This paper cites Cleaning noisy labels by negative ensemble learning for source-free unsupervised do- main adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Cleaning noisy labels by negative ensemble learning for source-free unsupervised do- main adaptation,

Reference 27

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This paper cites Uncertainty-aware pseudo-label filtering for source-free unsu- pervised domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Uncertainty-aware pseudo-label filtering for source-free unsu- pervised domain adaptation,

Reference 28

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This paper cites mixup: Beyond empirical risk minimization,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation mixup: Beyond empirical risk minimization,

Reference 29

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Cutmix: Regularization strategy to train strong classifiers with localizable features,

Reference 30

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Manifold mixup: Better representations by interpolating hidden states,

Reference 31

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This paper cites Token- mix: Rethinking image mixing for data augmentation in vision trans- formers,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Token- mix: Rethinking image mixing for data augmentation in vision trans- formers,

Reference 32

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Transmix: Attend to mix for vision transformers,

Reference 33

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This paper cites Fixbi: Bridging domain spaces for unsupervised domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Fixbi: Bridging domain spaces for unsupervised domain adaptation,

Reference 34

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This paper cites Patch-mix transformer for unsupervised domain adaptation: A game perspective,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Patch-mix transformer for unsupervised domain adaptation: A game perspective,

Reference 35

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This paper cites Adversarial domain adaptation with do- main mixup,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Adversarial domain adaptation with do- main mixup,

Reference 36

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This paper cites Prox- ymix: Proxy-based mixup training with label refinery for source-free domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Prox- ymix: Proxy-based mixup training with label refinery for source-free domain adaptation,

Reference 37

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This paper cites Understand- ing and improving source-free domain adaptation from a theoretical perspective,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Understand- ing and improving source-free domain adaptation from a theoretical perspective,

Reference 38

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Observation ce4e851c-b749-4454-beba-3374ed12e65a · outbound

This paper cites Sf(da)2: Source-free domain adaptation through the lens of data aug- mentation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Sf(da)2: Source-free domain adaptation through the lens of data aug- mentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:27.078470Z

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Observation 259149f6-5840-4fec-9a0c-dd77f48f6795 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Improved Baselines with Momentum Contrastive Learning

Reference 40

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This paper cites Deeper, broader and artier domain generalization,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Deeper, broader and artier domain generalization,

Reference 41

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This paper cites VisDA: The Visual Domain Adaptation Challenge.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation VisDA: The Visual Domain Adaptation Challenge

Reference 42

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

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Moment matching for multi-source domain adaptation,

Reference 43

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This paper cites Domain separation networks,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Domain separation networks,

Reference 44

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Observation 8b3b4c51-7256-4ab4-8d2b-da9be7ae13cc · outbound

This paper cites Unsupervised multi-target domain adaptation: An information theoretic approach,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Unsupervised multi-target domain adaptation: An information theoretic approach,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:26.401753Z

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Observation 43efa45d-19d7-40f4-bcdb-1e3af4ff7c8f · outbound

This paper cites Knowledge distilla- tion methods for efficient unsupervised adaptation across multiple do- mains,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Knowledge distilla- tion methods for efficient unsupervised adaptation across multiple do- mains,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:26.265755Z

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Observation 8c126844-605d-49b4-b4ae-c5afb3b446ca · outbound

This paper cites Sliced wasserstein discrepancy for unsupervised domain adap- tation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Sliced wasserstein discrepancy for unsupervised domain adap- tation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:26.072943Z

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Observation 6e1a9e49-b735-4650-86c1-ceffb17e97cf · outbound

This paper cites Contrastive adaptation network for unsupervised domain adaptation,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Contrastive adaptation network for unsupervised domain adaptation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:25.987507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed456428-74ba-44f6-a219-f2c9adce0047 · outbound

This paper cites Model adaptation: Unsupervised domain adaptation without source data,.

Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation Model adaptation: Unsupervised domain adaptation without source data,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:25.848057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 445e2ad3-54e8-4f00-a33a-858610c63eac · inbound