Paper Citation Record · LEDGER

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery

As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.03809.

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

pith.paper-citation-record.v1
1908.03809 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:06:43.765479Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

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

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b09bd1d3-3586-4718-ad2b-3176fbaf2caa · outbound

This paper cites ISPRS WG III/4. ISPRS 2D Semantic Labeling Contest.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery ISPRS WG III/4. ISPRS 2D Semantic Labeling Contest

Reference 1

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Observation c6520414-eba8-4c9d-ae6d-c4baa642268e · outbound

This paper cites xView: Objects in Context in Overhead Imagery.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery xView: Objects in Context in Overhead Imagery

Reference 2

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Observation c6a61708-4871-45a3-980e-e01be271e1ec · outbound

This paper cites SpaceNet competition.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery SpaceNet competition

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.365365Z

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Observation 89d8c030-9cae-4537-b50e-42715d037dea · outbound

This paper cites Dstl satellite imagery feature detection.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Dstl satellite imagery feature detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.349973Z

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Observation 41dc6bb9-50d7-490f-addf-c6424bb76d57 · outbound

This paper cites Learning active learning from data,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Learning active learning from data,

Reference 5

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Observation 5c2c42fd-7150-474f-af0d-80ddc4ab7dff · outbound

This paper cites Training deep networks with synthetic data: Bridging the reality gap by domain random- ization,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Training deep networks with synthetic data: Bridging the reality gap by domain random- ization,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.315994Z

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Observation a8aee62e-320c-4b57-b5e9-eeaf1feb2ac6 · outbound

This paper cites Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data

Reference 7

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Observation 0c86ddef-74a4-4851-b96f-a2fee637e289 · outbound

This paper cites Multimodal 3D Object Detection from Simulated Pretraining.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Multimodal 3D Object Detection from Simulated Pretraining

Reference 8

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Observation 70be4e68-dd71-46b6-b1f4-ffd198a3784b · outbound

This paper cites A data augmentation strategy based on simulated samples for ship detection in rgb remote sensing images,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery A data augmentation strategy based on simulated samples for ship detection in rgb remote sensing images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.300024Z

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Observation 9f83d082-e1ef-4fc4-8a4b-c4972c9cc4f3 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 10

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Source-reported events for the cited work

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Observation 8131f88d-1a2a-42ff-871e-c8f85a47df81 · outbound

This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 11

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Observation 79417565-ccab-4b45-aa97-84df9e0b7b20 · outbound

This paper cites Scargan: chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Scargan: chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.283339Z

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Observation 4d87af84-be87-45aa-8c5e-7cba00bdfaab · outbound

This paper cites Rendergan: Generating realistic labeled data,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Rendergan: Generating realistic labeled data,

Reference 13

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Observation ac351dda-0ed2-4383-8182-463bae7c0b7f · outbound

This paper cites Learning from simulated and unsupervised images through adversarial training,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Learning from simulated and unsupervised images through adversarial training,

Reference 14

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Observation 4d9d9678-f93e-4f02-a5e2-b440b1f1cc9e · outbound

This paper cites Evaluation of generative networks through their data augmentation capacity,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Evaluation of generative networks through their data augmentation capacity,

Reference 15

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Observation 035fa600-446a-45fd-a00a-ce67e6b87c2f · outbound

This paper cites Marta gans: Unsupervised representation learning for remote sensing image classiﬁcation,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Marta gans: Unsupervised representation learning for remote sensing image classiﬁcation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.211886Z

Source-reported events for the cited work

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Observation 37aed04f-d23c-4130-b097-b49ed5de7f69 · outbound

This paper cites Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning

Reference 17

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Observation e2717737-85b3-4c55-a5ef-580db5041e22 · outbound

This paper cites Wasserstein GAN.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Wasserstein GAN

Reference 18

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Observation d743eb79-cdc5-41b0-bc6b-00b33f6deb57 · outbound

This paper cites Pioneer Networks: Progressively Growing Generative Autoencoder.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Pioneer Networks: Progressively Growing Generative Autoencoder

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:06:43.827437Z

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Observation 072e394e-a64f-47ba-99e4-64389776e6e8 · outbound

This paper cites Improved training of wasserstein gans,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Improved training of wasserstein gans,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.194635Z

Source-reported events for the cited work

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Observation 7677a1c3-6040-43d4-88ba-08b2fe9fddfe · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Progressive growing of gans for improved quality, stability, and variation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.177979Z

Source-reported events for the cited work

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Observation 713dbd77-c1cb-4e8d-9b6e-ed9b5a7ee0a7 · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 22

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Observation f82e2a69-ef93-4ab5-ab02-112f70351f8e · outbound

This paper cites High-resolution image synthesis and semantic manipulation with conditional gans,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery High-resolution image synthesis and semantic manipulation with conditional gans,

Reference 23

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Observation b646f7c9-75ef-4f92-91ea-6a8571a70341 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmenta- tion,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery U-net: Convolutional networks for biomedical image segmenta- tion,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.131131Z

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Observation f5027771-ca38-44b9-97da-65f39b352dcb · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Image-to-image translation with conditional adversarial networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:44.114963Z

Source-reported events for the cited work

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Observation ecf290b3-5543-4f75-ada0-f3c18441f710 · outbound

This paper cites High-resolution image synthesis and semantic manipulation with conditional gans.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery High-resolution image synthesis and semantic manipulation with conditional gans

Reference 26

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Source-reported events for the cited work

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Observation 5deee87c-3d78-4675-a701-d08afb875d2d · outbound

This paper cites Automatic diﬀerentiation in pytorch,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Automatic diﬀerentiation in pytorch,

Reference 27

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Source-reported events for the cited work

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Observation 0ea2bb81-ba9a-4374-b7e6-76b5dbe198af · outbound

This paper cites Ssd: Single shot multibox detector,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Ssd: Single shot multibox detector,

Reference 28

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Observation 3a8e6966-d04b-41a9-9080-c4a2e42e3ade · outbound

This paper cites Microsoft coco: Common objects in context,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Microsoft coco: Common objects in context,

Reference 29

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Observation 71c8d221-c955-4ccc-ae7a-d6b0a785dc43 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 30

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Observation 8c9b8e14-84e2-4c8d-bf66-45cd42aa3b35 · outbound

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Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Deep learning face attributes in the wild,

Reference 31

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Observation 55606078-3312-46c2-801a-20cd4e62abe8 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:43.999255Z

Source-reported events for the cited work

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Observation 73d43c1c-50dd-4103-a9f1-916ab16507f4 · outbound

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

Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery Imagenet: A large-scale hierarchical image database,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:43.977987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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