Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers | Paper Citation Record

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This paper cites O’Connor, and Kevin McGuinness.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers O’Connor, and Kevin McGuinness

Reference 1

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This paper cites Cubuk, Alex Kurakin, Ki- hyuk Sohn, Han Zhang, and Colin Raffel.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Cubuk, Alex Kurakin, Ki- hyuk Sohn, Han Zhang, and Colin Raffel

Reference 2

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This paper cites Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel

Reference 3

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Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work

Reference 4

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

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning

Reference 5

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This paper cites Softmatch: Addressing the quantity-quality tradeoff in semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Softmatch: Addressing the quantity-quality tradeoff in semi-supervised learning

Reference 6

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Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work

Reference 7

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This paper cites Exploring simple siamese representation learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Exploring simple siamese representation learning

Reference 8

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This paper cites Semi- supervised learning under class distribution mismatch.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Semi- supervised learning under class distribution mismatch

Reference 9

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Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work

Reference 10

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This paper cites The cityscapes dataset for semantic urban scene under- standing.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers The cityscapes dataset for semantic urban scene under- standing

Reference 11

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This paper cites Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V

Reference 12

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This paper cites Imagenet: A large-scale hierarchical image database.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Imagenet: A large-scale hierarchical image database

Reference 13

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This paper cites Semi- supervised learning via weight-aware distillation under class distribution mismatch.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Semi- supervised learning via weight-aware distillation under class distribution mismatch

Reference 14

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This paper cites Mutexmatch: Semi-supervised learning with mutex- based consistency regularization.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Mutexmatch: Semi-supervised learning with mutex- based consistency regularization

Reference 15

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This paper cites Ssb: Simple but strong baseline for boosting performance of open-set semi- supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Ssb: Simple but strong baseline for boosting performance of open-set semi- supervised learning

Reference 16

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This paper cites Semi-supervised learning by entropy minimization.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Semi-supervised learning by entropy minimization

Reference 17

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This paper cites Safe deep semi-supervised learning for unseen-class unlabeled data.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Safe deep semi-supervised learning for unseen-class unlabeled data

Reference 18

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This paper cites Binary decomposition: A problem transformation perspective for open-set semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Binary decomposition: A problem transformation perspective for open-set semi-supervised learning

Reference 19

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This paper cites Deep residual learning for image recognition.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Deep residual learning for image recognition

Reference 20

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This paper cites Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data

Reference 21

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This paper cites SAFER- STUDENT for safe deep semi-supervised learning with unseen-class unlabeled data.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers SAFER- STUDENT for safe deep semi-supervised learning with unseen-class unlabeled data

Reference 22

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This paper cites Using self-supervised learning can improve model robustness and un- certainty.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Using self-supervised learning can improve model robustness and un- certainty

Reference 23

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This paper cites Trash to treasure: Harvesting OOD data with cross-modal matching for open-set semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Trash to treasure: Harvesting OOD data with cross-modal matching for open-set semi-supervised learning

Reference 24

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This paper cites They are not completely use- less: Towards recycling transferable unlabeled data for class-mismatched semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers They are not completely use- less: Towards recycling transferable unlabeled data for class-mismatched semi-supervised learning

Reference 25

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This paper cites Label propagation for deep semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Label propagation for deep semi-supervised learning

Reference 26

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This paper cites Unknown-aware graph regularization for robust semi-supervised learn- ing from uncurated data.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unknown-aware graph regularization for robust semi-supervised learn- ing from uncurated data

Reference 27

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This paper cites Learning multiple layers of features from tiny images.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Learning multiple layers of features from tiny images

Reference 28

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Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work

Reference 29

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This paper cites Temporal ensembling for semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Temporal ensembling for semi-supervised learning

Reference 30

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This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 31

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This paper cites Diversify and disam- biguate: Learning from underspecified data.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Diversify and disam- biguate: Learning from underspecified data

Reference 32

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This paper cites Diversify and dis- ambiguate: Out-of-distribution robustness via disagreement.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Diversify and dis- ambiguate: Out-of-distribution robustness via disagreement

Reference 33

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Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work

Reference 34

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This paper cites Iomatch: Simpli- fying open-set semi-supervised learning with joint inliers and outliers utilization.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Iomatch: Simpli- fying open-set semi-supervised learning with joint inliers and outliers utilization

Reference 35

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This paper cites Rethinking safe semi-supervised learning: Transferring the open- set problem to a close-set one.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Rethinking safe semi-supervised learning: Transferring the open- set problem to a close-set one

Reference 36

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 37

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Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Goodfellow

Reference 38

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This paper cites A threshold selection method from gray-level his- tograms.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers A threshold selection method from gray-level his- tograms

Reference 39

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This paper cites Openmatch: Open-set consistency regularization for semi-supervised learning with outliers.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Openmatch: Open-set consistency regularization for semi-supervised learning with outliers

Reference 40

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This paper cites Regulariza- tion with stochastic transformations and perturbations for deep semi- supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Regulariza- tion with stochastic transformations and perturbations for deep semi- supervised learning

Reference 41

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Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Fixmatch: Simplifying semi-supervised learning with consis- tency and confidence

Reference 42

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This paper cites Graph-based semi-supervised learning: A comprehensive review.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Graph-based semi-supervised learning: A comprehensive review

Reference 43

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Observation 04bf9a01-3680-48b5-986a-d4777138b66f · outbound

This paper cites The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop

Reference 44

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

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 45

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This paper cites USB: A unified semi-supervised learning benchmark for classification.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers USB: A unified semi-supervised learning benchmark for classification

Reference 46

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This paper cites Freematch: Self-adaptive thresholding for semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Freematch: Self-adaptive thresholding for semi-supervised learning

Reference 47

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Observation 015c71f0-8189-41bc-a459-c00ad0dda360 · outbound

This paper cites Out-of-distributed semantic pruning for robust semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Out-of-distributed semantic pruning for robust semi-supervised learning

Reference 48

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Observation 1d4ca92f-a05a-43a7-baa9-33b71885050d · outbound

This paper cites Scomatch: Alleviating overtrusting in open-set semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Scomatch: Alleviating overtrusting in open-set semi-supervised learning

Reference 49

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Observation 15394abd-0254-4fcf-b90b-74110ff5efb3 · outbound

This paper cites Hovy, Thang Luong, and Quoc Le.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Hovy, Thang Luong, and Quoc Le

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:29:25.240731Z

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Observation 9ea48db4-7096-4e56-a61d-ed5c04969bd6 · outbound

This paper cites Self-training for class- incremental semantic segmentation.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Self-training for class- incremental semantic segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:29:24.996433Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ab41d61-2854-4a62-9db5-0f7324a6fd55 · outbound

This paper cites Multi-task curriculum framework for open-set semi-supervised learning.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Multi-task curriculum framework for open-set semi-supervised learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:29:24.754748Z

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Observation 66d634d1-229e-4cbb-976b-9690f09518ba · outbound

This paper cites Wide residual networks.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Wide residual networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:29:24.524741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 14ca24c5-48fd-4261-9bfc-308e7f1779d6 · outbound

This paper cites Flexmatch: Boosting semi- supervised learning with curriculum pseudo labeling.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Flexmatch: Boosting semi- supervised learning with curriculum pseudo labeling

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:29:24.231593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation da586b3a-57f6-4297-a3d0-8ae834f5ad56 · outbound

This paper cites Simmatchv2: Semi-supervised learning with graph consistency.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Simmatchv2: Semi-supervised learning with graph consistency

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:29:24.005146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 917714fd-aafd-4e1a-ace4-08bbe9da3ad0 · outbound

This paper cites Simmatch: Semi-supervised learning with similarity match- ing.

Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Simmatch: Semi-supervised learning with similarity match- ing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:29:23.771232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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