Paper Citation Record · LEDGER

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.24093.

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

pith.paper-citation-record.v1
2506.24093 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:28:38.843577Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

32 of 32 outbound references displayed

verified exact8
verified fuzzy1
unresolved19
parse uncertain0
malformed identifier2
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 484ddcf6-6955-4dc7-b8dd-979625ca61ff · outbound

This paper cites IEEE Access12, 15642–15650 (2024).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies IEEE Access12, 15642–15650 (2024)

Reference 1

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:28:42.575174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 40114a43-de2a-4bdc-a10d-b46bc9342eaa · outbound

This paper cites https://doi.org/10.34808/RCZA-JY08.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies https://doi.org/10.34808/RCZA-JY08

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T21:28:40.699272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57c8e8c6-3b58-474c-9536-7569d49a5f8f · outbound

This paper cites Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:28:40.460200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f8740d05-30c4-497e-926c-0d280c8b3ec4 · outbound

This paper cites Dataset of Industrial Metal Objects.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Dataset of Industrial Metal Objects

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:35.140745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b15fb765-2589-44dd-a152-bd1c09421f8d · outbound

This paper cites In: International Conference on Learning Representations (ICLR) (2021),https: //openreview.net/forum?id=YicbFdNTTy.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: International Conference on Learning Representations (ICLR) (2021),https: //openreview.net/forum?id=YicbFdNTTy

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:35.282156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aec52ec5-1d15-4a8b-9ed9-5f9a3d1fa61a · outbound

This paper cites ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:28:40.222697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 384970d4-d412-475d-a22c-fafe2f8e0b87 · outbound

This paper cites Sensors 21(23), 7901 (Nov 2021).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Sensors 21(23), 7901 (Nov 2021)

Reference 7

Resolution: verified exact; doi, observed 2026-08-06T21:28:39.959756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5611f24d-b034-4bb2-aa38-117d1f032d25 · outbound

This paper cites Biological Cybernetics 36(4), 193–202 (Apr 1980).https://doi.org/10.1007/bf00344251.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Biological Cybernetics 36(4), 193–202 (Apr 1980).https://doi.org/10.1007/bf00344251

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:35.659269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166673a2-6fe0-4353-8c4d-ddfc619a0c2b · outbound

This paper cites Automation in Construc- tion 149, 104771 (May 2023).https://doi.org/10.1016/j.autcon.2023.104771.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Automation in Construc- tion 149, 104771 (May 2023).https://doi.org/10.1016/j.autcon.2023.104771

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:35.781923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c8c90c4-f89c-4c4d-bfe9-84f7d6901be7 · outbound

This paper cites Frontiers in Plant Science15 (Sep 2024).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Frontiers in Plant Science15 (Sep 2024)

Reference 10

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:28:42.146387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 162709bb-1f77-49ac-b270-44fa99a89c68 · outbound

This paper cites an unresolved cited work.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:36.034224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feb6ad2c-85ff-4bcd-8311-77cb66b04361 · outbound

This paper cites In: 2019 19th International Conference on Advanced Robotics (ICAR).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2019 19th International Conference on Advanced Robotics (ICAR)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:36.124374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96f8c398-6c1d-4e84-8a70-8ddad5abbfa7 · outbound

This paper cites an unresolved cited work.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:28:43.342412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 325c4813-e6f0-4c46-bed5-fc887830ced3 · outbound

This paper cites Springer International Publish- ing (2021).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Springer International Publish- ing (2021)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:36.422077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f60a139-9ffb-4f47-80bc-dd2ff0f6a810 · outbound

This paper cites https://doi.org/10.48550/ARXIV.1907.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies https://doi.org/10.48550/ARXIV.1907

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:36.593400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 688ae445-5311-47d8-b3f9-b1443ecc01c2 · outbound

This paper cites In: 2019 IEEE/CVF International Conference 20 P.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2019 IEEE/CVF International Conference 20 P

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:36.716706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2eccac0-b163-4d1c-b4d5-98d16f3c8c1c · outbound

This paper cites In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:36.866089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9572e131-55d6-43ff-be43-8c958da0d199 · outbound

This paper cites In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:36.975625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd38452f-bcbe-41e2-9260-e1724eeb41ab · outbound

This paper cites Object Detection Using Deep CNNs Trained on Synthetic Images.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Object Detection Using Deep CNNs Trained on Synthetic Images

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:28:39.714123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6607ae38-01c8-4a0c-a55c-566f81c5ce9c · outbound

This paper cites In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:37.300645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c087b7aa-2d8d-4a06-9d8d-5849847acb11 · outbound

This paper cites In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:37.432166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 346abe6d-8eca-4e56-9a09-4aa9f59a3a17 · outbound

This paper cites an unresolved cited work.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Unresolved cited work

Reference 22

Resolution: verified exact; doi, observed 2026-08-06T21:28:39.383378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 403a5d2d-6cfc-4c6d-a869-39dd80323a5d · outbound

This paper cites Applied Sciences15(1), 354 (Jan 2025).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Applied Sciences15(1), 354 (Jan 2025)

Reference 23

Resolution: verified exact; doi, observed 2026-08-06T21:28:39.108771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 348178b1-7928-4bdc-a739-def2678dd887 · outbound

This paper cites Springer International Publishing, 2nd edn.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Springer International Publishing, 2nd edn

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:37.791402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dd8ef74-5c3c-4ff7-9886-8d7058f2281a · outbound

This paper cites In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:37.891491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48c7bef7-4815-498e-bc30-ee4692ba9e99 · outbound

This paper cites In: 33rd British Machine Vision Confer- ence 2022, BMVC 2022, London, UK, November 21-24, 2022.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 33rd British Machine Vision Confer- ence 2022, BMVC 2022, London, UK, November 21-24, 2022

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:28:43.122185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ac9b748-4bcf-4048-8fc9-cc54e488f083 · outbound

This paper cites In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:38.179319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b8688bb-f26c-4ad5-921c-131c60a494f3 · outbound

This paper cites In: 2022 International Conference on Smart Systems and Technologies (SST).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2022 International Conference on Smart Systems and Technologies (SST)

Reference 28

Resolution: verified exact; raw_fallback, observed 2026-08-06T21:28:41.374732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5fa0e30-247a-4b25-8fdc-90db64fada81 · outbound

This paper cites In: Informatik in der Land-, Forst-und Ernährungswirtschaft-Fokus: Biodiversität fördern durch digitale Landwirtschaft.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: Informatik in der Land-, Forst-und Ernährungswirtschaft-Fokus: Biodiversität fördern durch digitale Landwirtschaft

Reference 29

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:28:42.867767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f8cac4fd-d46f-40ae-a774-302bd323580f · outbound

This paper cites Neurocomputing 312, 135–153 (Oct 2018).https://doi.org/10.1016/j.neucom.2018.05.083.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies Neurocomputing 312, 135–153 (Oct 2018).https://doi.org/10.1016/j.neucom.2018.05.083

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:38.586913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5805fa6-162b-46a7-b096-6af45197fcdd · outbound

This paper cites In: Proceedings of the 37th International Conference on Machine Learning.

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: Proceedings of the 37th International Conference on Machine Learning

Reference 31

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:28:41.063072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aaa6d89c-3512-4bf8-bb25-569865eed33f · outbound

This paper cites In: 2017 IEEE International Con- ference on Computer Vision (ICCV).

Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies In: 2017 IEEE International Con- ference on Computer Vision (ICCV)

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:38.843577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.