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Paper Citation Record · LEDGER

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization

As of 14 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2412.08479.

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

pith.paper-citation-record.v1
2412.08479 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

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measured 63 of 63 standing notices

One-hop event checks from named stored sources.

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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

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation 828fd144-dcc5-4821-93c9-caebe6cd2934 · outbound

This paper cites Adaptive consistency regular- ization for semi-supervised transfer learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Adaptive consistency regular- ization for semi-supervised transfer learning

Reference 1

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Observation 64f4c72f-ea67-4139-bdac-f21110b008aa · outbound

This paper cites Self-training: A survey.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Self-training: A survey

Reference 2

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Observation 39adb4cb-3520-440a-88af-9806b1e438d1 · outbound

This paper cites Learn- ing with pseudo-ensembles.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Learn- ing with pseudo-ensembles

Reference 3

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Observation 92179f40-f122-4c52-aa40-3cb68b97a91b · outbound

This paper cites Metareg: Towards domain generalization using meta- regularization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Metareg: Towards domain generalization using meta- regularization

Reference 4

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Observation e29cfd96-299f-479a-b408-1ed0c992a119 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Mixmatch: A holistic approach to semi-supervised learning

Reference 5

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Observation 8406f2da-8c2e-43e4-a602-6f890dd520cb · outbound

This paper cites AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 6

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Observation aedd9225-95e9-4cc5-8c96-54b929d8297e · outbound

This paper cites Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning

Reference 7

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Observation 20890600-b586-4fa8-95c1-973ec03f8b16 · outbound

This paper cites Domain generalization by mutual-information regu- larization with pre-trained models.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain generalization by mutual-information regu- larization with pre-trained models

Reference 8

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Observation 4aca3534-3e97-4ffe-80f9-38a4943d2321 · outbound

This paper cites Debiased self-training for semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Debiased self-training for semi-supervised learning

Reference 9

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Observation cc702479-10d0-49e2-9662-eed1550ff963 · outbound

This paper cites Self-training avoids using spurious features under domain shift.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Self-training avoids using spurious features under domain shift

Reference 10

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Observation 5dec8873-a25e-433d-9cc1-a2145a74dffe · outbound

This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 11

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Observation 6655cb23-f5d3-4756-99d6-75f98a3316f5 · outbound

This paper cites Deep domain generalization with structured low-rank constraint.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Deep domain generalization with structured low-rank constraint

Reference 12

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Observation cbe054e5-5b35-4707-807b-95dc52d8766f · outbound

This paper cites Domain gener- alization with domain-specific aggregation modules.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain gener- alization with domain-specific aggregation modules

Reference 13

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Observation 43d568de-1a2a-4eb4-be34-fb4eac77ded5 · outbound

This paper cites Domain-adversarial training of neural networks.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain-adversarial training of neural networks

Reference 14

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Source-reported events for the cited work

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Observation bd1569a7-0458-4fb5-b07c-5d7e07ec1f77 · outbound

This paper cites Unsupervised Representation Learning by Predicting Image Rotations.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Unsupervised Representation Learning by Predicting Image Rotations

Reference 15

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Source-reported events for the cited work

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Observation 25fb23fe-65dc-417a-9dec-3207bdd123c6 · outbound

This paper cites Semi-supervised learning by entropy minimization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Semi-supervised learning by entropy minimization

Reference 16

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Observation 90082c6a-44a1-4d35-ace9-7f5be435ee35 · outbound

This paper cites Class-imbalanced semi- supervised learning with adaptive thresholding.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Class-imbalanced semi- supervised learning with adaptive thresholding

Reference 17

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Observation cc26e991-af64-4070-aedd-f5454150b06d · outbound

This paper cites Semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Semi-supervised learning

Reference 18

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Observation 7db7cd7e-b79c-47c9-949c-e8b2a127b473 · outbound

This paper cites Deep residual learning for image recognition.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Deep residual learning for image recognition

Reference 19

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Observation 8168eb07-8ed4-497c-9f9e-45690e056066 · outbound

This paper cites Self-challenging improves cross-domain generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Self-challenging improves cross-domain generalization

Reference 20

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Observation f847dc2c-ef76-4450-b697-124b6d919134 · outbound

This paper cites Domain- guided weight modulation for semi-supervised domain gen- eralization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain- guided weight modulation for semi-supervised domain gen- eralization

Reference 21

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Observation 2e9a24d7-69a9-41d1-baf8-3fefecc21065 · outbound

This paper cites Dual student: Breaking the limits of the teacher in semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Dual student: Breaking the limits of the teacher in semi-supervised learning

Reference 22

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Observation b72ef058-c37d-407e-9bd0-33c0ab957718 · outbound

This paper cites Supervised contrastive learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Supervised contrastive learning

Reference 23

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Observation 891d4e0f-f0ed-493b-b9d1-3104cbda6bd3 · outbound

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CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Selfreg: Self-supervised contrastive regu- larization for domain generalization

Reference 24

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Observation 8a67269e-6e15-4049-95ad-1e969b9a33c0 · outbound

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CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Semi-supervised learning

Reference 25

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This paper cites Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works

Reference 26

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This paper cites Deeper, broader and artier domain generaliza- tion.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Deeper, broader and artier domain generaliza- tion

Reference 27

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This paper cites Domain generalization for med- ical imaging classification with linear-dependency regular- ization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain generalization for med- ical imaging classification with linear-dependency regular- ization

Reference 28

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This paper cites Selective-supervised contrastive learning with noisy labels.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Selective-supervised contrastive learning with noisy labels

Reference 29

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This paper cites Domain generalization via conditional invari- ant representations.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain generalization via conditional invari- ant representations

Reference 30

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Observation 6998bb78-812e-45c2-a7e8-f636b7449e3f · outbound

This paper cites Feature-critic networks for heterogeneous do- main generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Feature-critic networks for heterogeneous do- main generalization

Reference 31

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This paper cites Smooth neighbors on teacher graphs for semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Smooth neighbors on teacher graphs for semi-supervised learning

Reference 32

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This paper cites Towards recognizing unseen categories in un- seen domains.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Towards recognizing unseen categories in un- seen domains

Reference 33

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Source-reported events for the cited work

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This paper cites Robust place categorization with deep do- main generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Robust place categorization with deep do- main generalization

Reference 34

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Source-reported events for the cited work

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Observation 4df13c88-6b4a-446f-8a14-1384f7a1c517 · outbound

This paper cites Domain generalization via invariant fea- ture representation.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain generalization via invariant fea- ture representation

Reference 35

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:48.917532Z digest=sha256:b3d6e2534b8b4bd558699d829c4fe79922325f8e0ca054eaa6bb4ed171c69ab8

Observation 28bf8d5e-6927-48e7-ae3e-46ef86517173 · outbound

This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T17:48:48.922577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:48:48.922577Z digest=sha256:383e04513cca4b29a42a6934597e5e9c5daeb0abcd873fcc02288d21d3cbe8c9

Observation 341312eb-1cc4-4652-b4a7-b9c95e1abff1 · outbound

This paper cites Multi-objective interpolation train- ing for robustness to label noise.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Multi-objective interpolation train- ing for robustness to label noise

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-11T17:48:50.100758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:48.929201Z digest=sha256:a6c97a33d52e735fb2d6206a32375a219177559451557faca83c6f535e45faf8

Observation afb725ee-8148-49ec-b595-f1915576cc8f · outbound

This paper cites Mul- timatch: Multi-task learning for semi-supervised domain generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Mul- timatch: Multi-task learning for semi-supervised domain generalization

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-11T17:48:50.076522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:48.933965Z digest=sha256:a971316341186119a300628e4d0c54d1f6a51bc307cc18c4302fc28e7d1ba06f

Observation 1abb6269-966a-4b0a-863f-a37c370bce9a · outbound

This paper cites Learning to optimize domain specific normalization for domain generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Learning to optimize domain specific normalization for domain generalization

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:50.051881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:48.938896Z digest=sha256:b21073c55810cc7ec69362275cf38f1d7422c75f322780b6be282499f1b38b0a

Observation f4732ef3-5141-42f5-8e31-dd75e8a220b9 · outbound

This paper cites Generalizing Across Domains via Cross-Gradient Training.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Generalizing Across Domains via Cross-Gradient Training

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-11T17:48:48.945223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:48:48.945223Z digest=sha256:0199b9171e3d43d594f4171017455fc325edd9d97cd471a7b51a726b5f5d5495

Observation 1e90df95-861d-4a82-a3ad-af520497c2b3 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:50.027294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:48.950555Z digest=sha256:0590871e1d8032ca1459541ff8351ddd56c6047f7fa1d01283854abbc6d5a95a

Observation ec0a211f-9904-483f-8d39-680cdddae1aa · outbound

This paper cites Measuring domain shift for deep learning in histopathology.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Measuring domain shift for deep learning in histopathology

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:50.010750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:48.998267Z digest=sha256:2b93741f7d19d9f56bdde49470673aaceabbcf1a5d3c66e4407b74cd562c9156

Observation 0117791c-52da-4e03-9560-532627fb5a5a · outbound

This paper cites Sinkhorn label allocation: Semi-supervised classification via annealed self-training.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Sinkhorn label allocation: Semi-supervised classification via annealed self-training

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.992055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.070863Z digest=sha256:560bdacbadf41745b848e1b8ae39bc71249392871870ffd8d131a643d743786f

Observation 6127ed79-1b4e-4089-a7de-fdcce5ab26aa · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.971769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.084398Z digest=sha256:336121f24090ce71fa4ac6c745636ea4f82f26d049dd698491cfb959a1652690

Observation 66a16e1d-69bb-455c-b7c9-a5f77fc16c6a · outbound

This paper cites Inter- polation consistency training for semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Inter- polation consistency training for semi-supervised learning

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.943045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.089686Z digest=sha256:5cf184509cb150cae512c1e0c01f67fc5a781c750bbf189bdaf5178d44e42476

Observation 93ad11ae-b203-4590-ac24-c79ca612afd7 · outbound

This paper cites Generalizing to unseen domains: A survey on do- main generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Generalizing to unseen domains: A survey on do- main generalization

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T17:48:49.094868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:48:49.094868Z digest=sha256:082f45babfdb19611e4c5ef69fe104ec9173f34e424cf212076f3b4052dc9259

Observation eb31a839-2e79-46af-8b06-6a1cdf9f7cc1 · outbound

This paper cites Learning from extrinsic and intrinsic su- pervisions for domain generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Learning from extrinsic and intrinsic su- pervisions for domain generalization

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.905129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.100755Z digest=sha256:038aee049df20b856e98002790106c0d2cbf552df0ddc8d3708afc4d5047bed5

Observation edcb2053-7ae3-4361-aa94-c3c4433c2509 · outbound

This paper cites FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-11T17:48:49.107104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:48:49.107104Z digest=sha256:07f3ffeb04f9a01effffc497d4594687b94852eb6096b17f6e502edea01bd5df

Observation 72b20638-525d-409a-9253-9fba0fa8d8aa · outbound

This paper cites Crest: A class-rebalancing self-training frame- work for imbalanced semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Crest: A class-rebalancing self-training frame- work for imbalanced semi-supervised learning

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.886609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.148614Z digest=sha256:6bf096cb0f7d9e1036100bd5bc4fc73c4a7b09e57716e56eea69ca30920948e2

Observation 3e79b0ca-af3a-425a-9de9-0c7d84d6e2f0 · outbound

This paper cites Unsupervised data augmentation for consistency training.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Unsupervised data augmentation for consistency training

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.870683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.222704Z digest=sha256:ec179c7412cf7f9bb183285448ce7f03cc2783082789b0042363fa8aad6e6ec7

Observation ee439041-cecb-4576-a0af-17d818e8fb87 · outbound

This paper cites Dash: Semi-supervised learning with dynamic thresholding.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Dash: Semi-supervised learning with dynamic thresholding

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.852237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.254969Z digest=sha256:06e460e5c26387835735a3b7a9ff1a88d5f58d182631775409e6124b70d18661

Observation 4359cfce-c17a-4014-84f5-88d810bf37f8 · outbound

This paper cites Robust and Generalizable Visual Representation Learning via Random Convolutions.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Robust and Generalizable Visual Representation Learning via Random Convolutions

Reference 52

Resolution
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no resolver link, observed 2026-08-11T17:48:49.260701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:48:49.260701Z digest=sha256:616e4ccedfdbe8f15f9f37f51486c84d81fd9612ac1145e5ef3485fd6e56c4e8

Observation e3f3dea6-b203-4ad8-bec1-f97c36c4a972 · outbound

This paper cites A survey on deep semi-supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization A survey on deep semi-supervised learning

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.832972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.268134Z digest=sha256:093c2aeacd1e4c79f725dab85a0c9e67e2185c7ade348192cc0af3bcdb57f328

Observation 6be81d23-2236-4d63-9f37-3068d490434e · outbound

This paper cites Pcl: Proxy-based contrastive learning for domain generalization.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Pcl: Proxy-based contrastive learning for domain generalization

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.814838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.273451Z digest=sha256:9e2825cd1a3d6759797cd7e3975e9267cda6bdb76cc504b739aa6534e223ba64

Observation 17c7579d-3337-4144-ad23-76625f724408 · outbound

This paper cites Semi-supervised domain generalization with known and unknown classes.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Semi-supervised domain generalization with known and unknown classes

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.798994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.280141Z digest=sha256:b4dc8939cd3aebe1914ea29b1bb487c6e89704274376056d328dba78a2fa7b1f

Observation de37a347-28c7-41e4-b93a-fbe9d0f6eb27 · outbound

This paper cites Adaptive risk min- imization: Learning to adapt to domain shift.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Adaptive risk min- imization: Learning to adapt to domain shift

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.781468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.324555Z digest=sha256:bc8691c88760da016e68b93a703e44c5a6620e9b230e0afe7002caea9e77e99d

Observation 50fbc737-382f-4bec-af4b-7897d8d830e9 · outbound

This paper cites Lassl: Label-guided self-training for semi- supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Lassl: Label-guided self-training for semi- supervised learning

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.763527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.380069Z digest=sha256:6b67e6980e319f043a8c19efca40a78168197ba7e8584f252f816085a9026613

Observation 562cdde5-3f9d-443a-8f27-58276b853bd2 · outbound

This paper cites Domain generalization: A survey.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain generalization: A survey

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.745919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.394866Z digest=sha256:fd2ae71fa270f9b3c2b1912a63c85f791f5d3a9a852e85b042d742e19571054e

Observation a0fe3ad1-39dc-43af-93e5-15a7ab1e97b3 · outbound

This paper cites Semi-supervised domain generalization with stochastic stylematch.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Semi-supervised domain generalization with stochastic stylematch

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.727250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.401782Z digest=sha256:a0b899c4b8a4ceb400c3f5845b4540879c1ec91c2d2cf28c2ea8fb8307fbfd77

Observation c5a6787c-a0ed-49e5-a620-db2b96ba69f0 · outbound

This paper cites Deep domain-adversarial image generation for do- main generalisation.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Deep domain-adversarial image generation for do- main generalisation

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.708809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.406856Z digest=sha256:b150f6fd09e5f1c3da5d619c6ccf8cc58073c63c75935b835724ef59dba96707

Observation 1989e117-3b3e-4f75-8bbd-28c2709713dc · outbound

This paper cites Domain Generalization with MixStyle.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Domain Generalization with MixStyle

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-11T17:48:49.412517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:48:49.412517Z digest=sha256:6dbe873df08be1e36a7e8457e8d68e4ab626a293ce28338e8134d72ae1d6a6a8

Observation bc7444d6-9906-45f2-b4e8-c30b55385d03 · outbound

This paper cites Mixstyle neural networks for domain generalization and adaptation.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Mixstyle neural networks for domain generalization and adaptation

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.690264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.447356Z digest=sha256:5f4e14e5d3934b4381a3fe1b1de8dbaba0f98afb73ca3011e47f1fadb4fe7dc7

Observation b15c2071-f712-4873-b705-1f7692a8b204 · outbound

This paper cites Introduction to semi- supervised learning.

CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization Introduction to semi- supervised learning

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:48:49.669358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:48:49.499364Z digest=sha256:2ce3c293883a622bfebbb6d692df5f3a240d0d7969022cb9f940b348d806b44f

Pith citing papers

No inbound Pith citation observations are available.