Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T11:46:56.423328Z
Paper Citation Record · LEDGER
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
As of 18 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2509.02287.
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 2026-08-17T06:30:58.91139+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
34 of 34 outbound references displayed
- verified exact2
- verified fuzzy28
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7ff52d11-ff32-4bee-864a-4d43b29208f9 · outbound
This paper cites Semantic segmen- tation of autonomous driving scenes based on multi- scale adaptive attention.Neuroscience-driven Visual Representation, page 97, 2024.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Semantic segmen- tation of autonomous driving scenes based on multi- scale adaptive attention.Neuroscience-driven Visual Representation, page 97, 2024
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e06ca081-577a-4d47-a429-42e9981f873a · outbound
This paper cites Semantic segmentation of deep learningremotesensingimagesbasedonbandcombi- nation principle: Application in urban planning and land use.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Semantic segmentation of deep learningremotesensingimagesbasedonbandcombi- nation principle: Application in urban planning and land use
Reference 2
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4082e192-9191-49d2-852e-cca0fd4bed26 · outbound
This paper cites an unresolved cited work.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Unresolved cited work
Reference 3
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation be78b74b-564f-4568-9b2e-3b4212530894 · outbound
This paper cites Global and local texture random- ization for synthetic-to-real semantic segmentation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Global and local texture random- ization for synthetic-to-real semantic segmentation
Reference 4
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c8ce5681-f4af-4fe7-bd9e-4c0e2d270e92 · outbound
This paper cites Cutmix: Regularization strategy to train strongclas- sifiers with localizable features.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Cutmix: Regularization strategy to train strongclas- sifiers with localizable features
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:46:57.217932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fc869a3d-375c-48f2-a157-66f3039cfa34 · outbound
This paper cites Classmix: Segmentation-based data augmentation for semi-supervised learning.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Classmix: Segmentation-based data augmentation for semi-supervised learning
Reference 6
- Resolution
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- raw_fallback, observed 2026-08-05T11:46:57.179285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4d755de5-aa9f-4985-9704-51aa23541c96 · outbound
This paper cites Mic: Masked image consistency for context-enhanced domain adaptation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Mic: Masked image consistency for context-enhanced domain adaptation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:46:57.153786Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e42fc830-21b8-4775-b7c9-e1071bf6647a · outbound
This paper cites Pseudo-label guided contrastive learning for semi-supervised med- ical image segmentation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Pseudo-label guided contrastive learning for semi-supervised med- ical image segmentation
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:46:57.130207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ed564514-6112-498e-b7f0-eda13354d4e5 · outbound
This paper cites Contrastive learning and self- training for unsupervised domain adaptation in se- mantic segmentation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Contrastive learning and self- training for unsupervised domain adaptation in se- mantic segmentation
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:46:57.109795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 840557dc-20d5-435a-812e-5b70ebac2fa6 · outbound
This paper cites Probabilistic Test-Time Generalization by Variational Neighbor-Labeling.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Probabilistic Test-Time Generalization by Variational Neighbor-Labeling
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T11:46:56.631238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1ff15b04-6afa-4e36-b7bd-d3b1916b89c7 · outbound
This paper cites Daformer: Improving network architectures and training strategies for domain-adaptive semantic seg- mentation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Daformer: Improving network architectures and training strategies for domain-adaptive semantic seg- mentation
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:46:57.090356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 158696e6-3943-4b36-9c9f-fad61c8866fb · outbound
This paper cites Hrda: Context-aware high-resolution domain-adaptive se- mantic segmentation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Hrda: Context-aware high-resolution domain-adaptive se- mantic segmentation
Reference 12
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- raw_fallback, observed 2026-08-05T11:46:57.067642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a3ab0753-4078-4e68-897a-0395e81981a9 · outbound
This paper cites Prompt-based distribution alignment for un- supervised domain adaptation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Prompt-based distribution alignment for un- supervised domain adaptation
Reference 13
- Resolution
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- raw_fallback, observed 2026-08-05T11:46:57.037905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 50a45289-8ab9-441a-bd6a-23c1be86b674 · outbound
This paper cites Feature Alignment and Restoration for Domain Generalization and Adaptation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Feature Alignment and Restoration for Domain Generalization and Adaptation
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:46:56.265621Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e9819e8-dc6c-4ac6-90bd-84cf43b504cf · outbound
This paper cites Domain generalization with adversarial feature learning.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Domain generalization with adversarial feature learning
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:46:57.021962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6eb36b8d-8c0a-4aba-86cf-dcc3e0f0557b · outbound
This paper cites Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T11:46:56.570432Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 24b2587a-a7a9-424d-b8d5-aa1a94cb4f30 · outbound
This paper cites Unsupervised Domain Adaptation through Self-Supervision.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Unsupervised Domain Adaptation through Self-Supervision
Reference 17
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- unresolved
- no resolver link, observed 2026-08-05T11:46:56.291957Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe8b0926-7f2c-487f-ab2c-7a19a937cf07 · outbound
This paper cites Unsupervised batch- norm adaptation (ubna): A domain adaptation method for semantic segmentation without using source domain representations.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Unsupervised batch- norm adaptation (ubna): A domain adaptation method for semantic segmentation without using source domain representations
Reference 18
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- raw_fallback, observed 2026-08-05T11:46:57.005370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7d2b2383-5a0e-43d1-92de-25ac9f6b0e4b · outbound
This paper cites Domain generalization with small data.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Domain generalization with small data
Reference 19
- Resolution
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- raw_fallback, observed 2026-08-05T11:46:56.986022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 138d34a9-cbdf-4da6-97e1-e3ea64f8a91a · outbound
This paper cites Domaingeneralizationbylearningand removing domain-specific features.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Domaingeneralizationbylearningand removing domain-specific features
Reference 20
- Resolution
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- raw_fallback, observed 2026-08-05T11:46:56.966737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3600e0aa-bda8-4f40-8094-f5f8523f754d · outbound
This paper cites Feature stylization and domain- aware contrastive learning for domain generalization.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Feature stylization and domain- aware contrastive learning for domain generalization
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:46:56.945274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7cdc7105-e02f-4df2-aae9-0013389c3f73 · outbound
This paper cites Domain generalization through meta-learning: a survey.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Domain generalization through meta-learning: a survey
Reference 22
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- raw_fallback, observed 2026-08-05T11:46:56.922982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fbb7258e-2c4e-4e99-82dc-4dfdbad3d502 · outbound
This paper cites Multi- ple domain-adversarial ensemble learning for domain generalization.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Multi- ple domain-adversarial ensemble learning for domain generalization
Reference 23
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- raw_fallback, observed 2026-08-05T11:46:56.902743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1d8faa42-0a1f-437d-bd1f-f5b9fccd2ff9 · outbound
This paper cites Schwonberg, F.
Reference 24
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- raw_fallback, observed 2026-08-05T11:46:56.879250Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fd03a3e7-a953-4233-89ea-26a876064edf · outbound
This paper cites Style-hallucinated dual consis- tency learning for domain generalized semantic seg- mentation.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Style-hallucinated dual consis- tency learning for domain generalized semantic seg- mentation
Reference 25
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- raw_fallback, observed 2026-08-05T11:46:56.857093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7da2c33f-6cbd-462f-878e-6f6e838dd6fc · outbound
This paper cites Cbda: Contrastive-based data aug- mentation for domain generalization.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Cbda: Contrastive-based data aug- mentation for domain generalization
Reference 26
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- raw_fallback, observed 2026-08-05T11:46:56.828703Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bbd12768-0b3f-4033-8fe7-2b9dd4eed10d · outbound
This paper cites Style blind domain generalized semantic segmentation via covariance alignment and semantic consistence contrastive learning.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Style blind domain generalized semantic segmentation via covariance alignment and semantic consistence contrastive learning
Reference 27
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- raw_fallback, observed 2026-08-05T11:46:56.801799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0a848f6c-aa8c-4e09-a84e-b7e81f8f84ff · outbound
This paper cites Deep residual learning for image recog- nition.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Deep residual learning for image recog- nition
Reference 28
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9e72f166-4bd1-4b45-9e9b-481dc90baae4 · outbound
This paper cites Representation Learning with Contrastive Predictive Coding.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Representation Learning with Contrastive Predictive Coding
Reference 29
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df1f6e99-0098-42b0-844b-e01199944bc3 · outbound
This paper cites Richter et al.
Reference 30
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c91265d6-3e17-4651-8053-b1dd80264321 · outbound
This paper cites The synthia dataset: A large col- lectionofsyntheticimagesforsemanticsegmentation of urban scenes.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images The synthia dataset: A large col- lectionofsyntheticimagesforsemanticsegmentation of urban scenes
Reference 31
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- raw_fallback, observed 2026-08-05T11:46:56.731077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b88ffe16-f975-4a19-9a2c-7f07b106b73a · outbound
This paper cites The cityscapes dataset for se- mantic urban scene understanding.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images The cityscapes dataset for se- mantic urban scene understanding
Reference 32
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- raw_fallback, observed 2026-08-05T11:46:56.699658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d9c83d6e-aed1-4335-891d-3b8302a8dc8a · outbound
This paper cites Idd: A dataset for exploring problems ofautonomous navigation in unconstrained environments.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Idd: A dataset for exploring problems ofautonomous navigation in unconstrained environments
Reference 33
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- raw_fallback, observed 2026-08-05T11:46:56.675206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 912cbca3-d509-44a5-a1db-0c6b5150d748 · outbound
This paper cites Piva, Daan De Geus, and Gijs Dubbel- man.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images Piva, Daan De Geus, and Gijs Dubbel- man
Reference 34
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- raw_fallback, observed 2026-08-05T11:46:56.655235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.