Paper Citation Record · LEDGER

Certainty and Uncertainty Guided Active Domain Adaptation

As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2505.19421.

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

pith.paper-citation-record.v1
2505.19421 v1

Coverage vector

measured 58 of 58 reference resolution

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measured 60 of 60 standing notices

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T12:08:04.296880Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation 92822085-facd-47d9-9439-c9d991e532d8 · outbound

This paper cites Certainty and Uncertainty Guided Active Domain Adaptation.

Certainty and Uncertainty Guided Active Domain Adaptation Certainty and Uncertainty Guided Active Domain Adaptation

Reference 1

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This paper cites Gaussian Processes (GP) are non- parametric probabilistic models that generate uncertainty- aware predictions, making them suitable for semi-supervised and active learning [11, 12].

Certainty and Uncertainty Guided Active Domain Adaptation Gaussian Processes (GP) are non- parametric probabilistic models that generate uncertainty- aware predictions, making them suitable for semi-supervised and active learning [11, 12]

Reference 2

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Observation 8b74d92f-0692-49ed-9c47-7616dab53cdd · outbound

This paper cites Pseudo-Label based Certain Sampling Existing Active Learning (AL) methods focus on selecting uncertain samples while ignoring confident ones.

Certainty and Uncertainty Guided Active Domain Adaptation Pseudo-Label based Certain Sampling Existing Active Learning (AL) methods focus on selecting uncertain samples while ignoring confident ones

Reference 3

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Certainty and Uncertainty Guided Active Domain Adaptation Unresolved cited work

Reference 4

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This paper cites For the PLCS phase, we initialize κ = 1 and increase it by 1 per round, resulting in 15% certain pseudo-labels used during training.

Certainty and Uncertainty Guided Active Domain Adaptation For the PLCS phase, we initialize κ = 1 and increase it by 1 per round, resulting in 15% certain pseudo-labels used during training

Reference 5

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Observation 26f356df-64f9-43d5-9696-cfd51577b402 · outbound

This paper cites We conduct extensive abla- tion studies to assess the effectiveness of each component in our method, reporting results in Table 3 on the Office-Home dataset.

Certainty and Uncertainty Guided Active Domain Adaptation We conduct extensive abla- tion studies to assess the effectiveness of each component in our method, reporting results in Table 3 on the Office-Home dataset

Reference 6

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Certainty and Uncertainty Guided Active Domain Adaptation Unresolved cited work

Reference 7

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This paper cites A theory of learning from different domains,.

Certainty and Uncertainty Guided Active Domain Adaptation A theory of learning from different domains,

Reference 8

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Observation 4f8a67a8-26b7-424d-a77f-2bf6a3132476 · outbound

This paper cites Adversarial discriminative domain adapta- tion,.

Certainty and Uncertainty Guided Active Domain Adaptation Adversarial discriminative domain adapta- tion,

Reference 9

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Observation a30ce03d-d8a4-4808-960c-9772bbdcaf6e · outbound

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

Certainty and Uncertainty Guided Active Domain Adaptation Cycada: Cycle-consistent adversarial domain adaptation,

Reference 10

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Observation 34104a9d-78dd-47fc-9366-999749556ed0 · outbound

This paper cites Unsupervised pixel-level domain adaptation with generative adversar- ial networks,.

Certainty and Uncertainty Guided Active Domain Adaptation Unsupervised pixel-level domain adaptation with generative adversar- ial networks,

Reference 11

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This paper cites Active adversarial domain adaptation,.

Certainty and Uncertainty Guided Active Domain Adaptation Active adversarial domain adaptation,

Reference 12

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This paper cites Learning distinctive margin toward active domain adaptation,.

Certainty and Uncertainty Guided Active Domain Adaptation Learning distinctive margin toward active domain adaptation,

Reference 13

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Observation 58c5babb-04dc-4d33-afd3-111e9ddeecb2 · outbound

This paper cites Transferable query selection for active domain adaptation,.

Certainty and Uncertainty Guided Active Domain Adaptation Transferable query selection for active domain adaptation,

Reference 14

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Observation 4e75f87e-ddfe-487d-9667-78e225975e73 · outbound

This paper cites Active learning for domain adaptation: An energy-based approach,.

Certainty and Uncertainty Guided Active Domain Adaptation Active learning for domain adaptation: An energy-based approach,

Reference 15

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Observation 889091f5-35f7-4f81-b4ea-c88bc4cff338 · outbound

This paper cites Active domain adaptation via clustering uncertainty-weighted embeddings,.

Certainty and Uncertainty Guided Active Domain Adaptation Active domain adaptation via clustering uncertainty-weighted embeddings,

Reference 16

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Observation f7282b7a-f7ae-440c-a458-d9dcd7036fa8 · outbound

This paper cites 1, Springer, 2006.

Certainty and Uncertainty Guided Active Domain Adaptation 1, Springer, 2006

Reference 17

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This paper cites Syn2real transfer learning for image derain- ing using gaussian processes,.

Certainty and Uncertainty Guided Active Domain Adaptation Syn2real transfer learning for image derain- ing using gaussian processes,

Reference 18

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This paper cites Active learning with gaussian processes for object categorization,.

Certainty and Uncertainty Guided Active Domain Adaptation Active learning with gaussian processes for object categorization,

Reference 19

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Certainty and Uncertainty Guided Active Domain Adaptation Gaussian processes in machine learning,

Reference 20

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This paper cites Sentry: Selective entropy optimization via committee consistency for unsupervised domain adap- tation,.

Certainty and Uncertainty Guided Active Domain Adaptation Sentry: Selective entropy optimization via committee consistency for unsupervised domain adap- tation,

Reference 21

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This paper cites Deep residual learning for image recognition,.

Certainty and Uncertainty Guided Active Domain Adaptation Deep residual learning for image recognition,

Reference 22

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This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Certainty and Uncertainty Guided Active Domain Adaptation Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 23

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This paper cites Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Certainty and Uncertainty Guided Active Domain Adaptation Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Reference 24

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This paper cites Discrepancy-Based Active Learning for Domain Adaptation.

Certainty and Uncertainty Guided Active Domain Adaptation Discrepancy-Based Active Learning for Domain Adaptation

Reference 25

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

Certainty and Uncertainty Guided Active Domain Adaptation Moment matching for multi-source domain adaptation,

Reference 26

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This paper cites Deep hashing network for unsupervised domain adaptation,.

Certainty and Uncertainty Guided Active Domain Adaptation Deep hashing network for unsupervised domain adaptation,

Reference 27

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This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Certainty and Uncertainty Guided Active Domain Adaptation Pytorch: An imperative style, high-performance deep learning library,

Reference 28

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Certainty and Uncertainty Guided Active Domain Adaptation Imagenet: A large-scale hierarchical image database,

Reference 29

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Certainty and Uncertainty Guided Active Domain Adaptation An overview of gradient descent optimization algorithms

Reference 30

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Certainty and Uncertainty Guided Active Domain Adaptation Conditional adversarial domain adaptation,

Reference 31

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This paper cites Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets.

Certainty and Uncertainty Guided Active Domain Adaptation Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Reference 32

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This paper cites Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning.

Certainty and Uncertainty Guided Active Domain Adaptation Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning

Reference 33

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Certainty and Uncertainty Guided Active Domain Adaptation Active learn- ing for vision-language models,

Reference 34

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Certainty and Uncertainty Guided Active Domain Adaptation Active prompt learning in vision language models,

Reference 35

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Certainty and Uncertainty Guided Active Domain Adaptation Active finetun- ing: Exploiting annotation budget in the pretraining- finetuning paradigm,

Reference 36

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Certainty and Uncertainty Guided Active Domain Adaptation La- tent structured active learning,

Reference 37

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Certainty and Uncertainty Guided Active Domain Adaptation Entropic open-set active learning,

Reference 38

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Certainty and Uncertainty Guided Active Domain Adaptation Margin based active learning,

Reference 39

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Certainty and Uncertainty Guided Active Domain Adaptation Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning,

Reference 40

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This paper cites Representative sampling for text classification using support vector machines,.

Certainty and Uncertainty Guided Active Domain Adaptation Representative sampling for text classification using support vector machines,

Reference 41

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Certainty and Uncertainty Guided Active Domain Adaptation Submodularity in data subset selection and active learning,

Reference 42

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Certainty and Uncertainty Guided Active Domain Adaptation Unsupervised domain adaptation by backpropagation,

Reference 43

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Certainty and Uncertainty Guided Active Domain Adaptation Semi-supervised domain adaptation via minimax entropy,

Reference 44

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This paper cites Learning invariant representations and risks for semi-supervised domain adaptation,.

Certainty and Uncertainty Guided Active Domain Adaptation Learning invariant representations and risks for semi-supervised domain adaptation,

Reference 45

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Certainty and Uncertainty Guided Active Domain Adaptation Contrastive adaptation network for un- supervised domain adaptation,

Reference 46

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This paper cites Mind the class weight bias: Weighted maximum mean discrepancy for unsu- pervised domain adaptation,.

Certainty and Uncertainty Guided Active Domain Adaptation Mind the class weight bias: Weighted maximum mean discrepancy for unsu- pervised domain adaptation,

Reference 47

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Certainty and Uncertainty Guided Active Domain Adaptation Domain- adversarial training of neural networks,

Reference 48

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Certainty and Uncertainty Guided Active Domain Adaptation Gradient distribution alignment certificates better adversarial domain adapta- tion,

Reference 49

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Certainty and Uncertainty Guided Active Domain Adaptation Unsupervised domain adaptation for semantic segmen- tation via class-balanced self-training,

Reference 50

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This paper cites Class- imbalanced domain adaptation: an empirical odyssey,.

Certainty and Uncertainty Guided Active Domain Adaptation Class- imbalanced domain adaptation: an empirical odyssey,

Reference 51

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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 de4f9dc3-24e3-4919-8536-837280724ba3 · outbound

This paper cites Confidence regularized self-training,.

Certainty and Uncertainty Guided Active Domain Adaptation Confidence regularized self-training,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:17:51.029953Z

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Observation 67cad802-018a-4a2d-ab3d-e0052da9f238 · outbound

This paper cites Natural and Adversarial Error Detection using Invariance to Image Transformations.

Certainty and Uncertainty Guided Active Domain Adaptation Natural and Adversarial Error Detection using Invariance to Image Transformations

Reference 53

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Observation b455ce0f-771c-4f9f-b305-4da0a9cff7d5 · outbound

This paper cites Domain adaptation meets ac- tive learning,.

Certainty and Uncertainty Guided Active Domain Adaptation Domain adaptation meets ac- tive learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:17:50.769205Z

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Observation 495078a9-da80-441f-b6ec-12dbd418215e · outbound

This paper cites S3vaada: Submodular subset selec- tion for virtual adversarial active domain adaptation,.

Certainty and Uncertainty Guided Active Domain Adaptation S3vaada: Submodular subset selec- tion for virtual adversarial active domain adaptation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:17:50.602984Z

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Observation 1905c6f7-5857-42bf-9353-eb4880d5e647 · outbound

This paper cites Varying Certain Sampling Rate Fig.

Certainty and Uncertainty Guided Active Domain Adaptation Varying Certain Sampling Rate Fig

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:17:50.424584Z

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Observation 22d8b02a-a5ad-41b4-9dd4-73c646aedcc3 · outbound

This paper cites For the classifier, we ini- tialize weights using the Xavier initialization technique with no bias.

Certainty and Uncertainty Guided Active Domain Adaptation For the classifier, we ini- tialize weights using the Xavier initialization technique with no bias

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:17:50.262766Z

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Observation 0f664835-ee6c-48ed-a1b1-cf8fea9f6c4a · outbound

This paper cites CLUE [9] uses pre- dictive entropy for uncertainty estimation and then samples from different clusters that are weighted by entropy to im- pose diversity.

Certainty and Uncertainty Guided Active Domain Adaptation CLUE [9] uses pre- dictive entropy for uncertainty estimation and then samples from different clusters that are weighted by entropy to im- pose diversity

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:17:50.105987Z

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Pith citing papers

Observation 92822085-facd-47d9-9439-c9d991e532d8 · inbound