Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T20:43:56.798461Z
Paper Citation Record · LEDGER
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery
As of 20 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2501.19283.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T20:43:56.723686Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-09T20:43:56.867065Z
Reference resolution
18 of 18 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 3cb655a3-29c7-4d30-8627-c3deb0b2afec · outbound
This paper cites The fundamental problems that these models try to solve are, (i) to estimate the underlying distribution of the observed data and (ii) to generate sample data from the same.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery The fundamental problems that these models try to solve are, (i) to estimate the underlying distribution of the observed data and (ii) to generate sample data from the same
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 2026-08-20T06:33:59.587034+00:00.
Observation 1f9c76d4-c34d-4d3e-b904-53197386f844 · outbound
This paper cites Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery
Reference 2
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8890b368-96e9-4daf-a1fd-65212acb76ff · outbound
This paper cites an unresolved cited work.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery 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 2026-08-20T06:33:59.587034+00:00.
Observation 7116f21a-c052-4204-b160-c88949ce91f7 · outbound
This paper cites an unresolved cited work.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Unresolved cited work
Reference 4
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b5756d1f-fb5a-4012-8a0d-438ae345bec5 · outbound
This paper cites Deep gen- erative models: Survey,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Deep gen- erative models: Survey,
Reference 5
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 190f2ea5-930f-4a7e-92bf-c804bc1c0ed2 · outbound
This paper cites An introduction to deep generative modeling,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery An introduction to deep generative modeling,
Reference 6
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a9d4a532-51d8-4b55-ac01-9c823e1939cb · outbound
This paper cites Deep gaus- sian mixture models,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Deep gaus- sian mixture models,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T20:43:57.029055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0224c0ae-d2a2-4ada-ae3c-02fdd97fc588 · outbound
This paper cites Auto-Encoding Variational Bayes.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Auto-Encoding Variational Bayes
Reference 8
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ba2025e-f3e2-4926-896b-3f34c41f9cb7 · outbound
This paper cites Generative adversar- ial networks,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Generative adversar- ial networks,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T20:43:57.013972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 863e5b55-e6ff-4f92-9892-7841e0767fb0 · outbound
This paper cites Denoising diffusion probabilistic models,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Denoising diffusion probabilistic models,
Reference 10
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 48579103-f1e4-4519-b250-26763b519927 · outbound
This paper cites Understanding Diffusion Models: A Unified Perspective.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Understanding Diffusion Models: A Unified Perspective
Reference 11
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 539bca9b-8429-4491-9375-472c7ff1ecb3 · outbound
This paper cites Rose, Jacob J.
Reference 12
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 10ab5a76-c9de-4604-90b1-678857045d30 · outbound
This paper cites The kolmogorov-smirnov test for goodness of fit,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery The kolmogorov-smirnov test for goodness of fit,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T20:43:56.971754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9472a381-8774-49ba-8d35-c5338373af6f · outbound
This paper cites Ball divergence: Nonparametric two sample test,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Ball divergence: Nonparametric two sample test,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T20:43:56.955234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 35f5aa80-67d6-4d90-936d-2a2916874175 · outbound
This paper cites Multilayer feedforward networks are universal approx- imators,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Multilayer feedforward networks are universal approx- imators,
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e65f5411-b48d-46e0-8cf6-62b64c93d70b · outbound
This paper cites Deep learning,.
Reference 16
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 17f05f3b-92e2-4939-b47a-29a697d2401d · outbound
This paper cites 260–260, Springer US, Boston, MA, 2017.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery 260–260, Springer US, Boston, MA, 2017
Reference 17
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- verified fuzzy
- raw_fallback, observed 2026-08-09T20:43:56.906478Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 82bbc8a9-da13-4a76-8cd3-208741d6ecc2 · outbound
This paper cites A coefficient of agreement for nominal scales,.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery A coefficient of agreement for nominal scales,
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
Observation 1f9c76d4-c34d-4d3e-b904-53197386f844 · inbound
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery cites this paper.
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-08-09T20:43:56.875036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.