Paper Citation Record · LEDGER

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

As of 20 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

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measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

Reference 11

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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

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Reference 13

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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

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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

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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

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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

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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

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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

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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)

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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

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

Reference 22

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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

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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

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

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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

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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

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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

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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

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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

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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

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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

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Pith citing papers

No inbound Pith citation observations are available.