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Paper Citation Record · LEDGER

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift

As of 10 August 2026, this Paper Citation Record lists 100 of 113 outbound references and 0 inbound Pith citation observations for arXiv:2607.18072.

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

pith.paper-citation-record.v1
2607.18072 v1

Coverage vector

measured 100 of 113 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:19:07.949272Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

100 of 113 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved99
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9d74ff59-9ef2-43ef-9ff8-a42fd1067001 · outbound

This paper cites SIGKDD , pages =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift SIGKDD , pages =

Reference 1

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source=arxiv_source observed=2026-08-01T16:18:02.313724Z digest=sha256:c3cf88a0e05ab7c2bb170641f0bc293af2bb5528d94022d3b860fcb1f462e996

Observation a82a5623-1593-4607-a675-05accb38543e · outbound

This paper cites Towards Concise Models of Grid Stability , booktitle =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Towards Concise Models of Grid Stability , booktitle =

Reference 2

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source=arxiv_source observed=2026-08-01T16:19:07.397336Z digest=sha256:27da3b71cb9ea27073ee3a8b19c226b42e84cd5a79bd768d6c6f561080d28dd6

Observation 0675f8e6-fa26-4318-b944-905ec9ea4780 · outbound

This paper cites Multiclass classification of dry beans using computer vision and machine learning techniques , journal =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Multiclass classification of dry beans using computer vision and machine learning techniques , journal =

Reference 3

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source=arxiv_source observed=2026-08-01T16:19:07.438182Z digest=sha256:fc4e5c84edc2366a0704c072653e9614a61f62412adee8a6e7afb088d11eb3fa

Observation 459ff116-f627-40ae-abe1-99aae57fde4a · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-01T16:19:07.458056Z digest=sha256:05e1ca56bc6a5637b5d14e93b79f6283c886fbd99a9a6d795a001fb2568f2655

Observation 781a0c26-c068-46ae-9240-38c36f7b2949 · outbound

This paper cites Challenges in benchmarking stream learning algorithms with real-world data , journal =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Challenges in benchmarking stream learning algorithms with real-world data , journal =

Reference 5

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source=arxiv_source observed=2026-08-01T16:19:07.479295Z digest=sha256:a7edcc887a23610df476efbbf81ace52eae12bb36c0721483b707266b458879a

Observation f86379eb-6080-4428-a6a7-f47b41b53fe5 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-08-01T16:19:07.484311Z digest=sha256:6c61e5eb6b377821c1efb7e7317ee819117caa19590bf9a5a74cc1a8c1b7192d

Observation 3bf07703-63c6-4186-94bf-8dd2e1ecac03 · outbound

This paper cites Single-Domain Generalization in Medical Image Segmentation via Test-Time Adaptation from Shape Dictionary , booktitle =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Single-Domain Generalization in Medical Image Segmentation via Test-Time Adaptation from Shape Dictionary , booktitle =

Reference 7

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source=arxiv_source observed=2026-08-01T16:19:07.492250Z digest=sha256:f2d52ff6197a8c6b07f499fe994b01094093226ee96940d61b3c39c794f43e35

Observation 3dd339f7-3291-436a-a281-09cb5ee8d30b · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-01T16:19:07.498761Z digest=sha256:237cbf15310fd51aeaf8c21c22ffde135255856c57062040d0230c7bdc9057c7

Observation 5216f18d-baf1-4bf7-82a6-c47edb823ece · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-01T16:19:07.505340Z digest=sha256:7caea7e67c32501ebd562f3ede57f9e31fe79c21c6be11827626a62e99bad9cc

Observation 440e6634-5cc0-4761-bd39-c426f4e128c5 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-01T16:19:07.511826Z digest=sha256:0b7c0e6c7b3f67a0dc90afa8cc29920e136d2e16aca4a714adf40d508da395de

Observation 53f3d4c0-f4ed-4534-8de4-173d266abc01 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-01T16:19:07.517788Z digest=sha256:1fd9648ef4529c2256502977a575e3bda5343465375b2f440e2672b195814233

Observation 2ae73b5f-6df5-408a-ab6d-a24037d726b9 · outbound

This paper cites Modeling Tabular data using Conditional.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Modeling Tabular data using Conditional

Reference 12

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source=arxiv_source observed=2026-08-01T16:19:07.526682Z digest=sha256:2e3b4d456d7437b58c2377714d60545e3d36e3f77c4929b13847ad2208bd24b2

Observation e0d1b6ed-4278-4e46-95c6-298d16a4358f · outbound

This paper cites Conditional.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Conditional

Reference 13

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source=arxiv_source observed=2026-08-01T16:19:07.536507Z digest=sha256:42ab9ecab1aca4b0881e980610d69f2560635ff7743da866c359d904508a698d

Observation 7c253889-9003-45f1-baa8-7feafdeaef20 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 14

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source=arxiv_source observed=2026-08-01T16:19:07.547502Z digest=sha256:9e3504f7f979126b064821bdfe9af60986c6a970c5fbc639c392d854bc729081

Observation 459c54f7-354d-4741-b688-693eca2c5291 · outbound

This paper cites Classification Accuracy Score for Conditional Generative Models.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Classification Accuracy Score for Conditional Generative Models

Reference 15

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source=arxiv_source observed=2026-08-01T16:19:07.559228Z digest=sha256:4ad79cbca31000b986bea2165cbc720214b326eaf2c2187881d7ac6a2a99e213

Observation d55dc45f-bbb7-4aef-bd3d-ad08444d47ed · outbound

This paper cites MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification

Reference 16

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source=arxiv_source observed=2026-08-01T16:19:07.566270Z digest=sha256:cc7210ebbc8008ea0dbb91893a4b97fb0b5b17ac35d4b0fb2565464b68d85b93

Observation 4f48a03f-d468-4992-838b-fa84ca2bdfd4 · outbound

This paper cites Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (

Reference 17

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source=arxiv_source observed=2026-08-01T16:19:07.572638Z digest=sha256:ce6e0d749b2ac34c9ddfc2fd0ca7bd4909fc135abb683b2828d8534832bc039e

Observation 321a2d97-f4ff-42a1-ae2c-5982d1b356a5 · outbound

This paper cites 2020 , author =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift 2020 , author =

Reference 18

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source=arxiv_source observed=2026-08-01T16:19:07.576575Z digest=sha256:1a0b199883f9471460289694f58f536b60e661bb2267a521fa94b5571e774f29

Observation 0299025a-a7d6-40d9-b510-93597a96b237 · outbound

This paper cites NeurIPS Workshop , year=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NeurIPS Workshop , year=

Reference 19

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source=arxiv_source observed=2026-08-01T16:19:07.582911Z digest=sha256:73797de2e3d7f4acacff3c97c50eaa6e5f2f5e14eb6c71c15afa24317cb1ac39

Observation a59230b8-7ee8-4824-9151-db4687715c8d · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 20

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source=arxiv_source observed=2026-08-01T16:19:07.588182Z digest=sha256:0b394e9f476101621e8eb0d045148f1258ddffce68e04b4562e75ec6518e793b

Observation 8522d7a6-f565-4835-94d4-d494c9e1d49c · outbound

This paper cites Proceedings of the IEEE , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Proceedings of the IEEE , volume=

Reference 21

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source=arxiv_source observed=2026-08-01T16:19:07.594494Z digest=sha256:d00a4850fa51d9b6997521947d5ec050daaea850859cee53e9acc50efc507480

Observation 65ed6979-1246-4117-be60-d7f74946749d · outbound

This paper cites Understanding the Limitations of Conditional Generative Models , booktitle =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Understanding the Limitations of Conditional Generative Models , booktitle =

Reference 22

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source=arxiv_source observed=2026-08-01T16:19:07.599201Z digest=sha256:30d334debbc3b4aa999a21efb3069050b5dd2419d835bdb1cc247ff12e8a72a1

Observation c155ab55-34cb-4c2a-bcd2-2e0764695e90 · outbound

This paper cites SIGKDD , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift SIGKDD , pages=

Reference 23

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source=arxiv_source observed=2026-08-01T16:19:07.604917Z digest=sha256:a2f1933a8fb4210f8625fc2891f306a96f9f0fb7c557a3d8a8e404e08df91b9f

Observation 55ada273-c216-4e63-98ed-594d3523eb92 · outbound

This paper cites Neurocomputing , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Neurocomputing , volume=

Reference 24

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source=arxiv_source observed=2026-08-01T16:19:07.612180Z digest=sha256:5859b2e8ef2ff0555bc45f4435b84c53b4ba9c3f6d9191b14e41acd6c77a7ae7

Observation 3ed00e07-4067-4829-84c6-ba51d6b7125e · outbound

This paper cites ICCV , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICCV , pages=

Reference 25

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source=arxiv_source observed=2026-08-01T16:19:07.617985Z digest=sha256:33a8c8bc213178cab4b7487813f156430ff6b5ac541884b648ce14440e52e901

Observation 7fb2a79d-6561-45c9-ae41-52159958ef68 · outbound

This paper cites ICCV , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICCV , pages=

Reference 26

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source=arxiv_source observed=2026-08-01T16:19:07.622617Z digest=sha256:425dfefea1f2e8dcb491fa563e1be0c6de339832d0ba18d06ad4d9b4d889872d

Observation e42b29ba-ebb1-4ecf-ac91-a87aa644fede · outbound

This paper cites NIPS 2016 Tutorial: Generative Adversarial Networks.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NIPS 2016 Tutorial: Generative Adversarial Networks

Reference 27

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source=arxiv_source observed=2026-08-01T16:19:07.628406Z digest=sha256:62c5a3ca5653a5b118c6efa9eb671268c7b8c6ff0e7f83913e76efbb4fd43054

Observation 48249d86-490f-4069-9b36-86d7de3a5262 · outbound

This paper cites Survey of Generative Methods for Social Media Analysis.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Survey of Generative Methods for Social Media Analysis

Reference 28

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source=arxiv_source observed=2026-08-01T16:19:07.635533Z digest=sha256:0ea0d12307c78a907dd8b518cf4947e989c0b9039557df438aae4c1b9bd6518c

Observation ab8f53c8-a8ad-4f20-81f3-ca00f7ca4a1b · outbound

This paper cites ACM Computing Surveys , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ACM Computing Surveys , volume=

Reference 29

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source=arxiv_source observed=2026-08-01T16:19:07.642074Z digest=sha256:8f231a547088d77079142969255ce20a491a5687ae39399e4257242b898f3b6b

Observation 319d555b-091c-4ff8-9efa-c7ae3334515e · outbound

This paper cites ISCV , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ISCV , pages=

Reference 30

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source=arxiv_source observed=2026-08-01T16:19:07.651908Z digest=sha256:14cd3ad8e572be50430237617179df177bc664c012a5bc9d67998a3d5f6de8d0

Observation 92f45f02-f9cb-4f5c-bf9a-7bc22bb169c9 · outbound

This paper cites AI EDAM , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift AI EDAM , volume=

Reference 31

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source=arxiv_source observed=2026-08-01T16:19:07.661248Z digest=sha256:d71006e356ca6ad34504741a0c0d50a2b9c047ba78f179cd20e7c70c744c7383

Observation a70de847-4b5c-4ec6-822b-a92ad3bad037 · outbound

This paper cites ImageNet Classification with Deep Convolutional Neural Networks , volume =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ImageNet Classification with Deep Convolutional Neural Networks , volume =

Reference 32

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source=arxiv_source observed=2026-08-01T16:19:07.675803Z digest=sha256:d379c41a48f968571fae6e0e01bae97467d7c387e2b35bf90e37ce05c026f0fd

Observation 12f5bbbc-933e-455c-96f2-748db19a8f6e · outbound

This paper cites and Karypis, G.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift and Karypis, G

Reference 33

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source=arxiv_source observed=2026-08-01T16:19:07.682063Z digest=sha256:3f1cb489f91d9dc7deba0f3cf7d99731ec5f21154cebc27eb28765a12652c7db

Observation cf0d3729-5b8f-426c-b444-eb2cd6d57b1d · outbound

This paper cites Journal of the American Statistical association , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Journal of the American Statistical association , volume=

Reference 34

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source=arxiv_source observed=2026-08-01T16:19:07.686392Z digest=sha256:2a7ce700882d8d09d28b31162a5bd25e9fb92de9ac87e238f76e40d93118de14

Observation f982a269-508f-48ac-85f4-3f96714297fd · outbound

This paper cites NIPS , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NIPS , pages=

Reference 35

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source=arxiv_source observed=2026-08-01T16:19:07.691313Z digest=sha256:a0d60f62a5b539353f1f33756ddfa330f7491137ca70368558374cbd117b3ca4

Observation 42fd0795-f69c-4b71-a2c3-4ad5c064d35d · outbound

This paper cites Self-Tuning Spectral Clustering , booktitle =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Self-Tuning Spectral Clustering , booktitle =

Reference 36

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source=arxiv_source observed=2026-08-01T16:19:07.696261Z digest=sha256:e79b462a55af2112ff55a5e99a498736c637b684a9a1be0bb4ef87756817e0ea

Observation 24131f7c-ccea-4978-8e77-9f08ae5f2e68 · outbound

This paper cites Cohen , title =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Cohen , title =

Reference 37

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source=arxiv_source observed=2026-08-01T16:19:07.701958Z digest=sha256:c528efb68ad246ba800d0b4ddce9277cebec286bd4422cb086af426468bd5bd0

Observation f959821b-0931-4712-9fbb-e6c0e4482490 · outbound

This paper cites 2012 , school=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift 2012 , school=

Reference 38

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source=arxiv_source observed=2026-08-01T16:19:07.708330Z digest=sha256:0b5c68a4ca3498c767817d428c41e556aa08e99d1d41717a26f85d4db7db1dc5

Observation 4633ead6-fe68-48e0-a403-dd10e7fec128 · outbound

This paper cites Applied intelligence , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Applied intelligence , volume=

Reference 39

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source=arxiv_source observed=2026-08-01T16:19:07.716297Z digest=sha256:fc163732144c965bab155598e11dd352ae06d54514bffdead69bbc422dc928bb

Observation 60878753-74fd-4baf-8616-d41ec8e8daac · outbound

This paper cites WWW , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift WWW , pages=

Reference 40

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source=arxiv_source observed=2026-08-01T16:19:07.731159Z digest=sha256:c3cc346c0d7504ffea008a0a2af4aa29e06ccc2a2c3e316b590b302893008965

Observation c719bfaa-b5df-4b34-b60d-ba096c3fd3a7 · outbound

This paper cites SIGKDD , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift SIGKDD , pages=

Reference 41

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source=arxiv_source observed=2026-08-01T16:19:07.737273Z digest=sha256:0640345d7dd20c64bc8d25e0253dc0aea586cc81276adf589bce376904c87362

Observation bfb23bf6-950b-4daa-b181-9ce7e3d84d7f · outbound

This paper cites IEEE Transactions on pattern analysis and machine intelligence , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift IEEE Transactions on pattern analysis and machine intelligence , volume=

Reference 42

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source=arxiv_source observed=2026-08-01T16:19:07.744244Z digest=sha256:f2cc70d25d1147389f3e01c7bf980f2bf1f041f93f12afa47c55ea701959b1fe

Observation 705de297-8f92-43ea-89b4-eb7216e43fbb · outbound

This paper cites NIPS , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NIPS , pages=

Reference 43

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source=arxiv_source observed=2026-08-01T16:19:07.749180Z digest=sha256:89d45956dcc0b1c2a378faa5fda3730b8dea79b0299b01f6d147fc656ae01277

Observation 665be2fd-637b-4f37-bbb0-0b4735ef0e24 · outbound

This paper cites ICDM , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICDM , pages=

Reference 44

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source=arxiv_source observed=2026-08-01T16:19:07.755328Z digest=sha256:a2b5f71e1fd7e01798f2ea5397392fbe9f9e831bcf3b56531f20f68f542fc5ea

Observation 24daa3ce-e3e9-40ed-9ae7-cc39713910e1 · outbound

This paper cites SIGKDD , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift SIGKDD , pages=

Reference 45

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source=arxiv_source observed=2026-08-01T16:19:07.760612Z digest=sha256:b91a995a4e5ea3952864851eb511452e8d05ba2d812180fd9c93bbc0d4159be4

Observation 721aff7a-436c-4d5c-9103-bb59a50ca4ed · outbound

This paper cites ICCV , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICCV , pages=

Reference 46

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source=arxiv_source observed=2026-08-01T16:19:07.774175Z digest=sha256:bbac718d75f1e8f89c0d797a5920cc4efaeee3cfe569a0821f5f1d30fb0ccc68

Observation f8b1e3a9-c8a6-47f7-bd6e-7b856be7632b · outbound

This paper cites CVPR , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift CVPR , pages=

Reference 47

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source=arxiv_source observed=2026-08-01T16:19:07.778759Z digest=sha256:b2fb29146873428839b6aaea7a95eb7f671f0674d5f716a1a11c3c5d231135bc

Observation 71ccbd66-2631-4e99-9ec6-6d4115897a1c · outbound

This paper cites The Journal of Machine Learning Research , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift The Journal of Machine Learning Research , volume=

Reference 48

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source=arxiv_source observed=2026-08-01T16:19:07.781414Z digest=sha256:8f58c84e1f60c5fb673aee2c3fc55c07acce28c68de98300e2921bf8f239d85f

Observation 44991c01-545b-468a-9dd0-0480c185fc78 · outbound

This paper cites , author=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift , author=

Reference 49

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source=arxiv_source observed=2026-08-01T16:19:07.784036Z digest=sha256:c9f224d18ce41707e107301ee8cfd185975a2eba1e79adf3cea7352ad2fba3b4

Observation c1390421-bf91-481a-8625-6539d13379eb · outbound

This paper cites ICML , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICML , pages=

Reference 50

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source=arxiv_source observed=2026-08-01T16:19:07.786699Z digest=sha256:fd963d31e01967a513605be580f1080d7b9d81a464b6a79823a08cddfab62551

Observation e7fa528e-b4d5-4e2e-b897-81181c149580 · outbound

This paper cites Pattern Recognition , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Pattern Recognition , volume=

Reference 51

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source=arxiv_source observed=2026-08-01T16:19:07.789921Z digest=sha256:6f4299ed8a326d033fbe1e737d257ca28e3384685d26377cbf911d0d56293f96

Observation c7ad4c8d-dea6-4df3-b328-2c2a015ce44e · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift IEEE Transactions on Geoscience and Remote Sensing , volume=

Reference 52

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source=arxiv_source observed=2026-08-01T16:19:07.793275Z digest=sha256:99beb438b112b0ad61d0a287d25e6158bee37e4433cfa3767559db0a74a4311e

Observation 5ee37895-3a1b-418a-ba09-0253639d871d · outbound

This paper cites Neurocomputing , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Neurocomputing , volume=

Reference 53

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source=arxiv_source observed=2026-08-01T16:19:07.797010Z digest=sha256:29efab99a27aa4f6c7666b0a972e58a5ae0fb388c6b6887d187f25cf39dc4a29

Observation a7829834-6acd-47a6-b4cb-ff54137d5954 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-01T16:19:07.799846Z digest=sha256:bd05c7d71734b0a2e2c8dda01fe2275b854786572e4949e49c7c5f59bbdf988f

Observation 627ca618-cef3-4c72-834e-65d1c9fffe89 · outbound

This paper cites CVPR , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift CVPR , pages=

Reference 55

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source=arxiv_source observed=2026-08-01T16:19:07.802595Z digest=sha256:191005c997d59f9212f1dab6bc6528b3aede4946e90cee54ec0a84a0b89e1308

Observation 53ec377b-4d69-467d-aae9-36765f7616c8 · outbound

This paper cites Instance-Conditioned.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Instance-Conditioned

Reference 56

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source=arxiv_source observed=2026-08-01T16:19:07.805369Z digest=sha256:af41fd943c821d30ad01143c3509480afe518030c42390b7761f1a6b2557da7c

Observation 740f05a8-eed1-4bcd-9062-049440ed0f90 · outbound

This paper cites CVPR , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift CVPR , pages=

Reference 57

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source=arxiv_source observed=2026-08-01T16:19:07.808182Z digest=sha256:43a288840c9d9047e74347375bff1de0dc2bcc39e7913bd28d8158c2afe40c62

Observation fe98bf31-3d9d-47b4-af52-846173f333cc · outbound

This paper cites NIPS , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NIPS , volume=

Reference 58

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source=arxiv_source observed=2026-08-01T16:19:07.811273Z digest=sha256:8456396cd1689eb0007c36846396eaa2563a7a46f9432219b507e747db102bc9

Observation ea7d18ae-06ac-4c89-84b0-a12275310240 · outbound

This paper cites ICML , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICML , pages=

Reference 59

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source=arxiv_source observed=2026-08-01T16:19:07.814175Z digest=sha256:67f524472e1fbcef7156e8315bbc6900beedbb60bede928aced3391da17e471b

Observation 7ef51de3-dfd4-4e64-be99-dbb6c4ba425f · outbound

This paper cites 2021 , isbn =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift 2021 , isbn =

Reference 60

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source=arxiv_source observed=2026-08-01T16:19:07.817027Z digest=sha256:d993c17b4f17ec3a9609ad8f3605f03bcdb4a2e39234f873dec31f5b723ffeb0

Observation 408f38c6-d463-4c17-97bb-a67f696f4757 · outbound

This paper cites NIPS , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NIPS , volume=

Reference 61

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source=arxiv_source observed=2026-08-01T16:19:07.820547Z digest=sha256:9e925ee78aaccea3d5481b77ecd8cabfb268b8202d002a7ccc06482273d0357a

Observation a0b7509d-c70a-42a9-8e2b-506824d2a9c6 · outbound

This paper cites Kingma and Max Welling , title =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Kingma and Max Welling , title =

Reference 62

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source=arxiv_source observed=2026-08-01T16:19:07.823440Z digest=sha256:546caf277d6b3ca32f7c7578bf6cc0543742e06d0e61ffaed706e7fcaeb6acd4

Observation 9f7e036c-c7a5-401a-837a-756f99fef3b8 · outbound

This paper cites Conditional Generative Adversarial Nets.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Conditional Generative Adversarial Nets

Reference 63

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source=arxiv_source observed=2026-08-01T16:19:07.826173Z digest=sha256:ecf40fa49792d70a4d9c31781b0df74c442ca7b7f55a320cc6fc0dc4711e709c

Observation 2cf5de85-a9fe-4805-84e3-2171f5cf055c · outbound

This paper cites NIPS , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NIPS , volume=

Reference 64

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source=arxiv_source observed=2026-08-01T16:19:07.829875Z digest=sha256:1dfba9f09c1fcba05cbd1eb61a28423e3d2d27eca70ce0fb974348efb642aeed

Observation 09485423-54af-4744-be8f-c9fb72796a4a · outbound

This paper cites Xing , title =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Xing , title =

Reference 65

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source=arxiv_source observed=2026-08-01T16:19:07.832798Z digest=sha256:2d7e7044b5ec345a2459e4b4ff1f41e7306b8fe3632c47415997c344e3ecd63c

Observation 508f450b-f20e-48f0-b573-ecb613c970cf · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 66

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source=arxiv_source observed=2026-08-01T16:19:07.836122Z digest=sha256:f761e783dad437617a1d88eecd89a01e3669d48e9b9e39b681ff591ac52ae5ae

Observation 08e8defc-007a-47a0-a445-09e82b5b1a41 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 67

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source=arxiv_source observed=2026-08-01T16:19:07.839350Z digest=sha256:2e4a96d4d4efc75e43a0f4f82c6004cbaf085a2eb007a2859e6bac2c56e2f032

Observation 682f4d88-cbd7-42db-a884-03afd1c73168 · outbound

This paper cites 2022 , issn =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift 2022 , issn =

Reference 68

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source=arxiv_source observed=2026-08-01T16:19:07.842621Z digest=sha256:afa70487c054c65df066b168931d0a05f0531809eb6caaa7e56dcd6b1de79e3a

Observation a77b2e15-2be4-42ca-a8bf-9576b638fa39 · outbound

This paper cites Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data

Reference 69

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source=arxiv_source observed=2026-08-01T16:19:07.845938Z digest=sha256:a315034dcc97f90b34705d406d5925b4086dc775ec6d337f6f7df9cadbb24a84

Observation 2342bcd2-f866-4f21-9b57-7818d0edcff5 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 70

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no resolver link, observed 2026-08-01T16:19:07.849142Z

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source=arxiv_source observed=2026-08-01T16:19:07.849142Z digest=sha256:771b5c7233bda847906a11a6461c578261849669520c304cfed596903912b0d9

Observation f2497e9e-4237-4cac-a9c7-587ae8c60434 · outbound

This paper cites ICML , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICML , pages=

Reference 71

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source=arxiv_source observed=2026-08-01T16:19:07.852342Z digest=sha256:bcacb611f65a2c67b3c4343caed18222ede1511435fe189eb5bd592c25cc34da

Observation 9ad6c995-654a-4cdb-bf6b-9e3942916e79 · outbound

This paper cites ICML , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICML , pages=

Reference 72

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source=arxiv_source observed=2026-08-01T16:19:07.856240Z digest=sha256:eaa13f603887f143c39d4c9561d2bbdb5b0a3657a2570fce286e20cd11917bb0

Observation 1e6cd242-7fd4-41f8-85c5-53caecf6d018 · outbound

This paper cites ICCV , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift ICCV , pages=

Reference 73

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source=arxiv_source observed=2026-08-01T16:19:07.859142Z digest=sha256:95c0f990d0f405c45ed3b5cd6fb001a60daaef1509cb34f8c4236f8640f9f7d1

Observation 37cf3616-9181-426e-8f5f-c1d0afeb1172 · outbound

This paper cites CVPR , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift CVPR , pages=

Reference 74

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source=arxiv_source observed=2026-08-01T16:19:07.862037Z digest=sha256:e413a0ef70eddfe102e623c1c9dbdecbeca2701197d67289ebd92557cb1b8668

Observation fcfa33bf-4dc6-416a-8d61-5d9803c08a0d · outbound

This paper cites WACV , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift WACV , pages=

Reference 75

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source=arxiv_source observed=2026-08-01T16:19:07.864894Z digest=sha256:faf5286aab40256e0b074ca13fa2744fd57372639cd3e336d0e35f1df2b1bdc3

Observation 3f653a7a-f895-4bf9-a2e8-991a546d2641 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 76

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T16:19:07.867852Z digest=sha256:d6d731e879c1de7756f944a6fc5c153d57bbd34a3ea00c567843cac4c65d1200

Observation 26e18f16-508e-4dfc-a537-577328d7ebcb · outbound

This paper cites , author=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift , author=

Reference 77

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no resolver link, observed 2026-08-01T16:19:07.871741Z

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source=arxiv_source observed=2026-08-01T16:19:07.871741Z digest=sha256:6cc0fe038660140db2d789d366d7b3a2e13b6889a135a054168bef0b8fc05bae

Observation 442f682f-226f-4ce1-aee4-888d2aede1d9 · outbound

This paper cites IEEE TKDE , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift IEEE TKDE , volume=

Reference 78

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source=arxiv_source observed=2026-08-01T16:19:07.874575Z digest=sha256:330709fbe60b5c4ece1027509f778ed77ce9221b3b7ef9087aec44906bebb971

Observation 5ef0d9b1-b178-4b2b-8132-b9f4e04eb2ad · outbound

This paper cites Cell , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Cell , volume=

Reference 79

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no resolver link, observed 2026-08-01T16:19:07.877858Z

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source=arxiv_source observed=2026-08-01T16:19:07.877858Z digest=sha256:88f97bd9355233757577ad8beddce052b5aea58eda63bb4cc0bd0579dc988b35

Observation 6a67db5f-b37f-4143-8550-322d1b0b23bf · outbound

This paper cites Cytometry Part A , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Cytometry Part A , volume=

Reference 80

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source=arxiv_source observed=2026-08-01T16:19:07.881223Z digest=sha256:7cde2381aa57c91682db70584b611e285613ff90f1f5c87376fae441fa385b04

Observation d1e1b004-98be-47a3-9026-62c7629df20c · outbound

This paper cites The 2nd diabetic retinopathy – grading and image quality estimation challenge , year =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift The 2nd diabetic retinopathy – grading and image quality estimation challenge , year =

Reference 81

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no resolver link, observed 2026-08-01T16:19:07.884734Z

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source=arxiv_source observed=2026-08-01T16:19:07.884734Z digest=sha256:3a761cbb49ee0a9fd9ae795eeabc31a44f62b93ff7a260339d9444e1121e8ef0

Observation 3a5a395f-c8e1-4c4d-973b-7861f0472509 · outbound

This paper cites NeurIPS , year=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NeurIPS , year=

Reference 82

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source=arxiv_source observed=2026-08-01T16:19:07.888567Z digest=sha256:8b9866c1c1cb74743c0f650d12ebad8686ab090e46b4cfd98ca365fbbcdc358d

Observation 7665252b-ebb4-4b5e-8fe5-99f5eea9724e · outbound

This paper cites NeurIPS , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NeurIPS , volume=

Reference 83

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source=arxiv_source observed=2026-08-01T16:19:07.892332Z digest=sha256:153cc63662f742de97b79d747c7929dbcb6216f993d0a6f0bf772c454203dbc2

Observation 9654e287-c0df-45d9-9317-0485536b3fdd · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 84

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source=arxiv_source observed=2026-08-01T16:19:07.895984Z digest=sha256:5192a10c36d032ecbd90d518daa2929606e8e802118bdf4985777acbaec8c536

Observation af983d90-54ba-47f9-9dea-5f7ba9e9ff55 · outbound

This paper cites NeurIPS , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift NeurIPS , volume=

Reference 85

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source=arxiv_source observed=2026-08-01T16:19:07.899494Z digest=sha256:49e95d9792a4dfb63cd03054baaf234e4a1eacb25105c0853b31fb0f70828e4f

Observation ef655ee6-708c-4fdf-a32e-5787687fec44 · outbound

This paper cites IEEE Transactions on Biomedical Engineering , year=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift IEEE Transactions on Biomedical Engineering , year=

Reference 86

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source=arxiv_source observed=2026-08-01T16:19:07.902007Z digest=sha256:165e4325898885ae2f6c6a6fb512f63d386c9b455a5922d1e2fd10e914da0820

Observation 1ade758c-19f3-4129-95c2-8982c1fb6461 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 87

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source=arxiv_source observed=2026-08-01T16:19:07.905147Z digest=sha256:a670db6317cbe009fae7b4daaa058f74b4fb7e2761bac995199da040b240d0dd

Observation e6c86891-4127-48a6-8e28-08361ba3c086 · outbound

This paper cites Clinical data sharing using Generative Adversarial Networks , volume =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Clinical data sharing using Generative Adversarial Networks , volume =

Reference 88

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source=arxiv_source observed=2026-08-01T16:19:07.909092Z digest=sha256:a200b43353f9fc0bc21ae984a897d38b292e48e4f7b0a26f6a83b15faeef2e23

Observation 4c813f83-e56c-4701-9fe6-c6196826e714 · outbound

This paper cites CoRR , volume =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift CoRR , volume =

Reference 89

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source=arxiv_source observed=2026-08-01T16:19:07.912114Z digest=sha256:62b0b335baec5a7de90e75d13fc3ec15090880ffdd5b2e226466f2d0a045ea11

Observation 3e6b2d03-96cc-46a5-85a7-332aeeb4ff3a · outbound

This paper cites CoRR , volume =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift CoRR , volume =

Reference 90

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source=arxiv_source observed=2026-08-01T16:19:07.915091Z digest=sha256:648a315c594f76b76d50a3fd2fc07d8c31e2ee7de2ad4e6161c9da264d63611c

Observation f5a62eca-2ce7-4212-8ebd-ff8fae15f6f1 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 91

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source=arxiv_source observed=2026-08-01T16:19:07.918293Z digest=sha256:788332f20402633a395ad8fb3720527e19a801aba5eca6145aa632c18e0977a0

Observation 176af5d2-26ed-41ee-bb6a-0e422fd642df · outbound

This paper cites Diffusion Models:.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Diffusion Models:

Reference 92

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source=arxiv_source observed=2026-08-01T16:19:07.921379Z digest=sha256:7ae946f0d1bc9a3635e44f291cf4199cf718ff7615f2f350579007750f74b297

Observation 75bfdb0f-a8eb-4569-ae39-08bbbc800893 · outbound

This paper cites Denoising Diffusion Probabilistic Models , booktitle =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Denoising Diffusion Probabilistic Models , booktitle =

Reference 93

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source=arxiv_source observed=2026-08-01T16:19:07.924512Z digest=sha256:d592c19b52a77b77520e9a5eb6e8d1b9af93a96818ea8395e897c6e92405d049

Observation ad1ef5f6-ddf0-4d0e-8579-b7dc2ea3169a · outbound

This paper cites Artificial Intelligence Review , year=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Artificial Intelligence Review , year=

Reference 95

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source=arxiv_source observed=2026-08-01T16:19:07.930371Z digest=sha256:22dc7a5d0ec4f3a7f30ccb7c3daf97f52f10bf5af2e7a525dce63172fbdb000a

Observation 0a4b15d8-83d7-415d-9449-a3d77ae5dc79 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , year=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

Reference 96

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source=arxiv_source observed=2026-08-01T16:19:07.933656Z digest=sha256:eb2fc8eb88de37916f34ac2f86127654195bfdda38c5dd3b7aabbf58a6c07f78

Observation 3887747d-7871-4977-8acc-50d47b7651e9 · outbound

This paper cites Proceedings of the VLDB Endowment , volume=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Proceedings of the VLDB Endowment , volume=

Reference 97

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source=arxiv_source observed=2026-08-01T16:19:07.937027Z digest=sha256:fe8066bf096cff4ab2fba4cdfc7727fa342d1da47a30aaaeca32991a9bf6cd7f

Observation e8ee8205-28f9-4948-8678-c248d0576a8b · outbound

This paper cites International Conference on Machine Learning , pages=.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift International Conference on Machine Learning , pages=

Reference 98

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source=arxiv_source observed=2026-08-01T16:19:07.940084Z digest=sha256:df5e9441b0135736fcf425a2fa7afa71dcd320e4a84762d9152bb0851a63516e

Observation 25a159df-72a8-41f2-85e3-4423130d4252 · outbound

This paper cites Conditional GANs with Auxiliary Discriminative Classifier , booktitle =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Conditional GANs with Auxiliary Discriminative Classifier , booktitle =

Reference 99

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source=arxiv_source observed=2026-08-01T16:19:07.943092Z digest=sha256:50d0d126c1a1025e1e1dffcc6e6501d9a314d02a293075fd7acba37e582bbff9

Observation 7a997e91-8cd3-4bd9-b477-c3008d2f1f3e · outbound

This paper cites 2023 , url =.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift 2023 , url =

Reference 100

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source=arxiv_source observed=2026-08-01T16:19:07.946247Z digest=sha256:e5cc6fafe221a681a78ceda20f58f49d5ad2abbb0b43a7774dd6d489a79c7a51

Observation ccc96568-dfa3-4261-99b0-d2c39920fd18 · outbound

This paper cites an unresolved cited work.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Unresolved cited work

Reference 101

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source=arxiv_source observed=2026-08-01T16:19:07.949272Z digest=sha256:4921da6be26f92b95b69c1cfac29c0e72aa0e269940090a8c7d8518ebda63219

Pith citing papers

No inbound Pith citation observations are available.