Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation | Paper Citation Record

Observation a4d46806-3def-47b3-9c37-025165842349 · outbound

This paper cites Analysis of representations for domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Analysis of representations for domain adaptation

Reference 1

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Observation af3ec01a-7107-4cf3-a562-0374170133c3 · outbound

This paper cites A theory of learning from different domains.Machine learning, 79: 151–175, 2010.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation A theory of learning from different domains.Machine learning, 79: 151–175, 2010

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.478160Z

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Observation a63a8ae8-1aa3-4c64-bd09-d09cb284910c · outbound

This paper cites Learning disentangled semantic representation for domain adaptation.IJCAI, 2019:2060–2066, 2019.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Learning disentangled semantic representation for domain adaptation.IJCAI, 2019:2060–2066, 2019

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.450484Z

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Observation 1a5db1d0-078f-4899-9d58-172c11f55cb4 · outbound

This paper cites Multi-prompt alignment for multi-source unsupervised domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Multi-prompt alignment for multi-source unsupervised domain adaptation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.422001Z

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Observation 0712bb69-e856-4f61-800e-9cb1e82e6bb2 · outbound

This paper cites Transferability vs.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Transferability vs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.396554Z

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Observation 3fce2c3a-d0bf-42a0-8f34-e63d5d3b13a9 · outbound

This paper cites Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.362660Z

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Observation eb8088d7-c50f-4231-950e-66cd424dc47b · outbound

This paper cites Gradually vanishing bridge for adversar- ial domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Gradually vanishing bridge for adversar- ial domain adaptation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.336806Z

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Observation ffa20d5c-5407-40b7-8403-6a4513aeda7a · outbound

This paper cites Domain-agnostic mutual prompting for unsuper- vised domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Domain-agnostic mutual prompting for unsuper- vised domain adaptation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.313067Z

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Observation 73ee26f8-3b8c-4234-89b5-8a08f560ac4b · outbound

This paper cites Partial feature selection and alignment for multi-source domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Partial feature selection and alignment for multi-source domain adaptation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.278283Z

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Observation 77fda9de-3ca5-4961-8c07-3804c33d2800 · outbound

This paper cites Stylegan-nada: Clip- guided domain adaptation of image generators.ACM Trans- actions on Graphics (TOG), 41(4):1–13, 2022.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Stylegan-nada: Clip- guided domain adaptation of image generators.ACM Trans- actions on Graphics (TOG), 41(4):1–13, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.255170Z

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Observation 93215950-cfcc-44a6-b9f0-41c57520bbf2 · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unsupervised domain adaptation by backpropagation

Reference 11

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Observation 753ab697-8342-4e6d-88ed-3510eab700ac · outbound

This paper cites Domain-adversarial training of neural networks.Journal of machine learning research, 17(59):1–35,.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Domain-adversarial training of neural networks.Journal of machine learning research, 17(59):1–35,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.188113Z

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Observation 02694157-4fad-439b-a97d-725033f78f5d · outbound

This paper cites Domain adaptation via prompt learning.IEEE Transactions on Neural Networks and Learning Systems, 2023.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Domain adaptation via prompt learning.IEEE Transactions on Neural Networks and Learning Systems, 2023

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.161516Z

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This paper cites Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.126734Z

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Observation 3937d309-4cad-4813-a86a-896bc4e18bc9 · outbound

This paper cites Rasch, Bernhard Sch¨olkopf, and Alexander J.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Rasch, Bernhard Sch¨olkopf, and Alexander J

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.092888Z

Source-reported events for the cited work

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Observation 2e05c1fa-1fe9-4e24-9d40-08c01eae787f · outbound

This paper cites Spherical space domain adaptation with robust pseudo-label loss.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Spherical space domain adaptation with robust pseudo-label loss

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.059495Z

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Observation 86376274-d186-401c-8369-219dda8f5049 · outbound

This paper cites Deep residual learning for image recognition.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Deep residual learning for image recognition

Reference 17

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Observation 6f6a3188-1dbe-41c8-ae1f-22555e1890fc · outbound

This paper cites Unsupervised domain adaptation with hierarchical gradient synchronization.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unsupervised domain adaptation with hierarchical gradient synchronization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:50.014030Z

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Observation 45ba509c-85cf-49c9-8b51-b1420f3684c9 · outbound

This paper cites Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.973893Z

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Observation 5f13124c-13d8-41c1-b229-d7cd3349130f · outbound

This paper cites Wilds: A benchmark of in-the-wild distribu- tion shifts.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Wilds: A benchmark of in-the-wild distribu- tion shifts

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.936816Z

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Observation 75e7bba0-7b8a-4a1f-ade7-cf0e2a8bdc1a · outbound

This paper cites Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.902533Z

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Observation 26fc9305-ff5a-483c-aea6-cf0d5d431a71 · outbound

This paper cites Empowering unsupervised domain adaptation with large- scale pre-trained vision-language models.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Empowering unsupervised domain adaptation with large- scale pre-trained vision-language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.873492Z

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Observation ebb694ca-ad49-4a75-a61e-204210223bd5 · outbound

This paper cites Sliced wasserstein discrepancy for unsu- pervised domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Sliced wasserstein discrepancy for unsu- pervised domain adaptation

Reference 23

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Observation 3f84203f-632a-4305-86e0-1fab8824f3cd · outbound

This paper cites Enhanced transport distance for unsuper- vised domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Enhanced transport distance for unsuper- vised domain adaptation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.819941Z

Source-reported events for the cited work

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Observation d1f38e62-f99e-49bb-bda6-3c0b3a07de06 · outbound

This paper cites T-svdnet: Exploring high-order prototypical correlations for multi-source domain adaptation.ICCV, 2021.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation T-svdnet: Exploring high-order prototypical correlations for multi-source domain adaptation.ICCV, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.775773Z

Source-reported events for the cited work

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Observation 5c26e7ba-e351-4aca-8757-e82c9f11d74e · outbound

This paper cites How to avoid machine learning pitfalls: a guide for academic researchers.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation How to avoid machine learning pitfalls: a guide for academic researchers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:39.895768Z

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Observation 1fa57695-7055-4f4c-b1d2-b46a64f71c6c · outbound

This paper cites Learning transferable features with deep adaptation networks.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Learning transferable features with deep adaptation networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.739575Z

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Observation 321cc92b-d80f-404b-b618-3b28e6c0cd84 · outbound

This paper cites Deep transfer learning with joint adaptation networks.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Deep transfer learning with joint adaptation networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.689797Z

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Observation 7689edc1-603d-4f37-8528-90fd16406ffb · outbound

This paper cites Transferable representation learning with deep adaptation networks.IEEE transactions on pattern analysis and machine intelligence, 41(12):3071–3085, 2018.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Transferable representation learning with deep adaptation networks.IEEE transactions on pattern analysis and machine intelligence, 41(12):3071–3085, 2018

Reference 29

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Observation b466da48-63c8-496b-b62f-d8f1dd995be7 · outbound

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Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unresolved cited work

Reference 30

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Observation c34c5e82-8576-4685-8a6a-86205a3e05bb · outbound

This paper cites Tidot: A teacher imitation learning approach for domain adaptation with optimal trans- port.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Tidot: A teacher imitation learning approach for domain adaptation with optimal trans- port

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.619885Z

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Observation b7874775-0aa5-4a14-aec5-96b40b142c82 · outbound

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

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Moment matching for multi-source domain adaptation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.597870Z

Source-reported events for the cited work

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Observation 5bf970a6-1318-4ca6-9432-d2350134ed03 · outbound

This paper cites an unresolved cited work.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unresolved cited work

Reference 33

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Observation d3075834-604c-4d3b-9b53-44fd51e44ef8 · outbound

This paper cites Global-local regularization via distribu- tional robustness.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Global-local regularization via distribu- tional robustness

Reference 34

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Observation 6cd55256-d9e9-40e7-8db3-3fd23fc9d0d5 · outbound

This paper cites Enhanc- ing domain adaptation through prompt gradient alignment.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Enhanc- ing domain adaptation through prompt gradient alignment

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.527481Z

Source-reported events for the cited work

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Observation 2bca71a0-81a6-43d2-bc86-c8fb45fc70a1 · outbound

This paper cites Control- lable prompt tuning for balancing group distributional robust- ness.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Control- lable prompt tuning for balancing group distributional robust- ness

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.506370Z

Source-reported events for the cited work

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Observation ee692b7f-a4fe-48e5-95bb-5722c4b6d28b · outbound

This paper cites Learning transferable visual models from natural language supervision.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Learning transferable visual models from natural language supervision

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.485488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9497e4c4-43a9-41b2-bfd5-1fc86209946f · outbound

This paper cites Multi-source unsupervised domain adaptation via pseudo target domain.IEEE Transactions on Image Process- ing, 2022.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Multi-source unsupervised domain adaptation via pseudo target domain.IEEE Transactions on Image Process- ing, 2022

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.462954Z

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Observation 7603c451-6276-4db7-b407-2cc4bfc09b5e · outbound

This paper cites Distributionally robust neural networks.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Distributionally robust neural networks

Reference 39

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Source-reported events for the cited work

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Observation 8d2412a0-3643-4e3d-92d5-f44c51a804b0 · outbound

This paper cites Ushiku, and T.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Ushiku, and T

Reference 40

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f2887ea-3028-4cf1-b9bc-1f9ec8671bb2 · outbound

This paper cites Wasserstein Distance Guided Representation Learning for Domain Adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Wasserstein Distance Guided Representation Learning for Domain Adaptation

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:43.764890Z

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Observation 33d6529c-8991-4a19-8d8a-26f3aaf40042 · outbound

This paper cites A dirt-t approach to unsupervised domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation A dirt-t approach to unsupervised domain adaptation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.232844Z

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Observation eb2f4f1e-770c-4608-821f-29ae540e30c6 · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Deep coral: Correlation alignment for deep domain adaptation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.145078Z

Source-reported events for the cited work

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Observation 0f22c2b8-2a8a-4688-b3b7-d145c2921a9a · outbound

This paper cites Unsupervised domain adaptation via structurally regularized deep clustering.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unsupervised domain adaptation via structurally regularized deep clustering

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:49.049791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2e87e78c-af94-4779-9334-274f2a672351 · outbound

This paper cites Unsupervised domain adaptation via distilled discriminative clustering.Pattern Recognition, 127:108638, 2022.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unsupervised domain adaptation via distilled discriminative clustering.Pattern Recognition, 127:108638, 2022

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:48.903035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 247076b4-8496-4b8f-95b1-7234c7492405 · outbound

This paper cites Simultaneous deep transfer across domains and tasks.CoRR,.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Simultaneous deep transfer across domains and tasks.CoRR,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:48.770886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 05591cad-fefb-4dba-9eac-24c362afa340 · outbound

This paper cites Adversarial discriminative domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Adversarial discriminative domain adaptation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:41.941249Z

Source-reported events for the cited work

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Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:09:48.633235Z

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Observation b3dc0fd3-d1c0-4c22-a017-4be5c8286bbc · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Deep hashing network for unsupervised domain adaptation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:48.452577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d51d8d43-8fa6-4d25-80c9-3fd79f01be41 · outbound

This paper cites Springer, Berlin, Heidelberg, 2008.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Springer, Berlin, Heidelberg, 2008

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:48.321134Z

Source-reported events for the cited work

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Observation b59095a9-fd4d-419f-9f66-284af8818990 · outbound

This paper cites Vector quan- tized Wasserstein auto-encoder.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Vector quan- tized Wasserstein auto-encoder

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:48.230104Z

Source-reported events for the cited work

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Observation d8996b8b-581c-4caf-a8dc-678a9521d665 · outbound

This paper cites Learning to combine: Knowledge aggregation for multi- source domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Learning to combine: Knowledge aggregation for multi- source domain adaptation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:48.025622Z

Source-reported events for the cited work

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Observation 32069ec9-1dc7-495d-a968-504230b07914 · outbound

This paper cites an unresolved cited work.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:09:47.789858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c421cee-5aa7-4574-9fd5-8cf5c8b02204 · outbound

This paper cites Zuo, Junjie Yan, and Liang Lin.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Zuo, Junjie Yan, and Liang Lin

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:47.388305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 36d9f97e-c623-4a06-9969-5a8d242a1f04 · outbound

This paper cites How transferable are features in deep neural networks?Ad- vances in neural information processing systems, 27, 2014.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation How transferable are features in deep neural networks?Ad- vances in neural information processing systems, 27, 2014

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:47.143017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 994d561a-08db-45d1-a0b5-7165d5996018 · outbound

This paper cites Autolabel: Clip-based framework for open-set video domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Autolabel: Clip-based framework for open-set video domain adaptation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:46.965183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1896f923-3c37-44ff-af0a-f78b7994bcfd · outbound

This paper cites Domain- symmetric networks for adversarial domain adaptation.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Domain- symmetric networks for adversarial domain adaptation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:46.664889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 59ad5163-33a9-47f6-a666-9190ecdfc8b8 · outbound

This paper cites Costeira, Jos´e M.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Costeira, Jos´e M

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:46.385647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5b5c9b7-a96a-4036-997e-d62719ee9514 · outbound

This paper cites an unresolved cited work.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:09:46.138032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 935d1b05-e397-42f4-94d9-952441eba7aa · outbound

This paper cites Multi-source distilling domain adapta- tion.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Multi-source distilling domain adapta- tion

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:45.910859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation acab9340-99b8-4d55-a340-2b55886d649f · outbound

This paper cites Learning to prompt for vision-language models.Interna- tional Journal of Computer Vision, 130(9):2337–2348, 2022.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Learning to prompt for vision-language models.Interna- tional Journal of Computer Vision, 130(9):2337–2348, 2022

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:45.665594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 747b182b-76b7-45f9-80ed-3ba09488174c · outbound

This paper cites Unsupervised domain adap- tion harnessing vision-language pre-training.IEEE Transac- tions on Circuits and Systems for Video Technology, 2024.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unsupervised domain adap- tion harnessing vision-language pre-training.IEEE Transac- tions on Circuits and Systems for Video Technology, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:45.450958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c40329b9-6061-4e3b-9598-e051750f6cba · outbound

This paper cites Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:45.188430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e58a2ff5-8579-416c-8680-4af651214961 · outbound

This paper cites Office-Home is a medium-scaled dataset containing approximately 15,500 images from 65 categories in four do- mains: Art, Clipart, Product, and Real World.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Office-Home is a medium-scaled dataset containing approximately 15,500 images from 65 categories in four do- mains: Art, Clipart, Product, and Real World

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.978849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8633a21b-e192-4185-9d27-b1d8edf2206c · outbound

This paper cites Since these alternative methods typically fine-tune many more parameters, we ex- clude them from the experiments to ensure a fair comparison.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Since these alternative methods typically fine-tune many more parameters, we ex- clude them from the experiments to ensure a fair comparison

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.742287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8d9ccdc1-dd03-4aa6-a688-fd9b2c8fe33e · outbound

This paper cites Distance-Aware Pseudo-Label As discuss in previous section, different transferability be- tween domains motivate us a distance aware pseudo-labels scheme.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Distance-Aware Pseudo-Label As discuss in previous section, different transferability be- tween domains motivate us a distance aware pseudo-labels scheme

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.495312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9a9c6058-8388-491b-a08b-a391e2898e6f · outbound

This paper cites Performance on Corrupted OfficeHome dataset.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Performance on Corrupted OfficeHome dataset

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.265943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fcc19fe9-02ca-4c23-8157-f75b1517ea7e · outbound

This paper cites Specifically, denote T={τ k T }K k=1 where τ k T represents the text embeddings of the context prompt [P k sh][P T ][CLASSk] for class k.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Specifically, denote T={τ k T }K k=1 where τ k T represents the text embeddings of the context prompt [P k sh][P T ][CLASSk] for class k

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.019227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4ee69ce-4955-48a9-b05e-91d33f0b4be0 · outbound

This paper cites an unresolved cited work.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Unresolved cited work

Reference 450

Resolution: parse uncertain; raw_fallback, observed 2026-08-07T04:09:49.099035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Reference resolution

Outbound references

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