Paper Citation Record · LEDGER

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

As of 16 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:1908.06472.

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

pith.paper-citation-record.v1
1908.06472 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:47:54.104722Z

measured 26 of 26 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

26 of 26 outbound references displayed

verified exact0
verified fuzzy16
unresolved10
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73477845-0543-4bd8-bd70-38cb42f00926 · outbound

This paper cites In: BTW (Workshops).

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: BTW (Workshops)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.501942Z

Source-reported events for the cited work

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Observation 98806f8b-bd2e-491b-a25d-e00673f7c979 · outbound

This paper cites An Analysis of Deep Neural Network Models for Practical Applications.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles An Analysis of Deep Neural Network Models for Practical Applications

Reference 2

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Source-reported events for the cited work

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Observation 2144c5b5-da6b-4f02-a61e-d70f62144955 · outbound

This paper cites bioRxiv p.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles bioRxiv p

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.486722Z

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Observation eb1146bc-5db7-48c5-822e-b349d302358f · outbound

This paper cites In: Proceedings of the International Conference on Agricultural Engineering, Aarhus, Denmark.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Proceedings of the International Conference on Agricultural Engineering, Aarhus, Denmark

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.471670Z

Source-reported events for the cited work

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Observation 387a5d4e-cd6d-4e4b-b1b9-2eb9f1621a50 · outbound

This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.456599Z

Source-reported events for the cited work

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Observation 95aa65c7-0bb2-400b-80b5-d934c7a78956 · outbound

This paper cites In: Advances in neural information processing systems.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Advances in neural information processing systems

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3edaa067-51c1-46dd-ad5c-fa6160999355 · outbound

This paper cites In: Machine Learning in the Environmental Sciences Workshop, in Proc.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Machine Learning in the Environmental Sciences Workshop, in Proc

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.430614Z

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.

Observation bdf0bda2-6f7b-421b-acee-8446b9950b2b · outbound

This paper cites In: Proc.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Proc

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.415224Z

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.

Observation c17d83fd-f744-44e5-8f95-e660eaf04d97 · outbound

This paper cites In: Disaster Management for Resilience and Public Safety Workshop, in Proc.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Disaster Management for Resilience and Public Safety Workshop, in Proc

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.400428Z

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.

Observation e2f2c8b3-6914-48f7-9e12-fe3bfefcbeac · outbound

This paper cites Com- puters and Electronics in Agriculture 147, 70–90 (2018).

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Com- puters and Electronics in Agriculture 147, 70–90 (2018)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.385642Z

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.

Observation 3a0373b5-a25c-4b8e-bca3-0e729c1943c1 · outbound

This paper cites Meta-Sim: Learning to Generate Synthetic Datasets.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Meta-Sim: Learning to Generate Synthetic Datasets

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22344887-523e-4d58-b9ed-c4e70418a5cd · outbound

This paper cites IEEE transactions on medical imaging 26(7), 1010–1016 (2007) 7 Unity.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles IEEE transactions on medical imaging 26(7), 1010–1016 (2007) 7 Unity

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.369848Z

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.

Observation ebacfb77-612a-4ac7-9127-158eb7a569cd · outbound

This paper cites In: Advances in neural information processing systems.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Advances in neural information processing systems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.352932Z

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.

Observation 85284631-fcc9-4280-bf11-28393888d011 · outbound

This paper cites Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption

Reference 14

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Source-reported events for the cited work

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Observation ece9e56f-87b0-4f50-a688-b9d8d53e2f2c · outbound

This paper cites Distill 3(3), e10 (2018).

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Distill 3(3), e10 (2018)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.337709Z

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.

Observation 819f5983-b6ab-4c31-b829-786a3d369a3e · outbound

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

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2209bd02-e179-4d91-9eb9-57175be01c2d · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.322494Z

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.

Observation eb6c3524-7d77-48fb-aa9a-991a41cecfdf · outbound

This paper cites Sensors 17(4), 905 (2017).

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Sensors 17(4), 905 (2017)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.305750Z

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.

Observation 8af3c52c-10c8-4c13-a23d-f37005e7739d · outbound

This paper cites In: European Conference on Computer Vision.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: European Conference on Computer Vision

Reference 19

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Source-reported events for the cited work

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Observation a967aa16-56f6-4b17-87bb-9ec53d912b7c · outbound

This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 20

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Observation 54f01f4c-8f80-46c8-9869-2846195b1063 · outbound

This paper cites Neural networks 61, 85–117 (2015).

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Neural networks 61, 85–117 (2015)

Reference 21

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Source-reported events for the cited work

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Observation 9df2542f-3310-46b5-a0b5-57009cf25850 · outbound

This paper cites Washigton: Microsoft Research (2017).

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Washigton: Microsoft Research (2017)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.255391Z

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.

Observation 099aedfc-6c8a-4d70-8ad7-856982b04fd9 · outbound

This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T12:47:54.090555Z

Source-reported events for the cited work

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Observation adb78561-761b-41bb-8471-0f724fbfc909 · outbound

This paper cites In: European conference on computer vision.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: European conference on computer vision

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:54.228168Z

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.

Observation 836293be-db8c-4836-a7f3-9cb2f8d917c7 · outbound

This paper cites Building Generalizable Agents with a Realistic and Rich 3D Environment.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles Building Generalizable Agents with a Realistic and Rich 3D Environment

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T12:47:54.099701Z

Source-reported events for the cited work

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Observation 63b36138-5bf4-49df-8c6c-f0a264dfb784 · outbound

This paper cites In: Proceedings of the IEEE interna- tional conference on computer vision.

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles In: Proceedings of the IEEE interna- tional conference on computer vision

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T12:47:54.104722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.