Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:29:23.083180Z
Paper Citation Record · LEDGER
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers
As of 7 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2505.24443.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
56 of 56 outbound references displayed
- verified exact1
- verified fuzzy48
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 70cff29c-b8cd-4ab9-9f07-11df694ed633 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:35.373632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation de7b9d08-babf-45a1-9735-6815e31eeaf8 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:35.086085Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation eb4786bf-cf2c-4efa-b907-89b899a16dd0 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:34.794289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 45ed394a-dbf3-4575-83f8-06efdec252be · outbound
This paper cites an unresolved cited work.
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:29:34.481118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation eb8d902d-4b6a-4105-bacc-b14db67d4572 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:34.219633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 158652e9-9eb7-46b1-be16-71291851f00f · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:33.836172Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1606d608-6c70-4504-969c-15c557b9529a · outbound
This paper cites an unresolved cited work.
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:29:33.631853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1d2fe95a-5dab-468a-a69a-a46cdec345c5 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:33.374401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b74711bf-5900-44fb-a99d-b2e88e3280ac · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:33.192237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation df920595-5d78-4fc2-8bad-e9a3d74bde70 · outbound
This paper cites an unresolved cited work.
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:29:32.916234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 051ca930-addc-41e4-aa9a-94e9df9434b5 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:32.715829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e2827b9e-648d-4b8b-828d-614f91af0583 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:32.553220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 980529c2-83ae-4c74-a89a-6fbd01cd9902 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:32.433723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 519d912f-4493-4b64-8db0-1920325cb2bc · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:32.267048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4567f0ee-1cf3-4a66-a709-636d131bf263 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:32.094338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3b3af617-4130-41f1-92a1-5bb9c860dff1 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:31.843459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 10659e81-ad1e-43d2-9a7e-5038f68b1e9e · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:31.656408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0e8eb46a-3f96-4b32-8b9b-aa9a86444c19 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:31.526148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e181c97c-1beb-4fd9-acf3-6d22352f71dd · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:31.387208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9fc4bd72-31d6-4be9-af47-58d3ac9dcda4 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:31.143974Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6c7bbefe-75ff-40b8-9e67-8da196b19d24 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:30.969880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0de19b7d-8c52-4577-a64d-f66081b187bb · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:30.843026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c7bfaf24-d990-4aed-8514-491a045c5681 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:30.736049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 626f1021-5291-4fbd-92b3-615b8eb3fa53 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:30.440224Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 7a5a3c3c-abdb-4fe1-a60b-53e6c20bff8e · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:30.171358Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation beca0523-1d40-474e-bb29-58380b634678 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:29.980220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 26dcde5d-8efc-4b5c-9a2d-93085458242c · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:29.731881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9f485a46-2b2a-4f97-8dbf-b15306ff7d2f · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:29.398443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e31f4c61-0900-455f-80cb-2e6f0b7cf619 · outbound
This paper cites an unresolved cited work.
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work
Reference 29
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:29:29.239297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 38eb2a9d-f090-4e78-970d-a37f0526819b · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:28.973030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ad154a59-adb8-4ca5-980c-d72bf991da43 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:28.788044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 128d00d1-4c0f-4853-96c4-16596ee9cef3 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:28.650031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c5f9b04e-83de-4048-b5ae-3e21904652d6 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:28.439966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4f9bd6dc-76b7-4d02-b3c4-4a9362157232 · outbound
This paper cites an unresolved cited work.
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Unresolved cited work
Reference 34
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:29:28.317426Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 30765845-4cbe-40b6-ba6d-04a11aaf8355 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:28.178041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c62be277-00ef-475a-8182-18ffd49e87a8 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:28.009046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 27037311-4bf6-48c1-b5d1-2c0d146c241c · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:29:20.534753Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 11e64503-8979-42dd-823b-5a583d61830c · outbound
This paper cites Goodfellow.
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers Goodfellow
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:27.745181Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6af75e5b-3377-4486-9dc6-ff92ccfc4fb8 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:27.496104Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 18d91cef-b07f-4c55-befb-1a167f088b92 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:27.344816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f21b6d00-40b7-4609-b16a-a005f2f39488 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:27.148077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation acabfbd4-c711-43b2-aa0a-ef6096621506 · outbound
This paper cites Fixmatch: Simplifying semi-supervised learning with consis- tency and confidence.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:26.969813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1976e2e0-d1cf-43db-93d5-86069a31e3ef · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:26.685428Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T12:29:23.571341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6e32e738-b673-4383-aea7-0c9cc59aecdc · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:29:21.746232Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 574623ad-19f7-43e7-a96a-9b15767b4296 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:26.389864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 236c7eb8-bbd4-4740-90f3-819aa75af0b6 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:26.119776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:25.885353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:29:25.554751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.