Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data | Paper Citation Record

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This paper cites International Journal of Computer Vision, 1–25 (2024).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data International Journal of Computer Vision, 1–25 (2024)

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This paper cites IEEE Transactions on Artificial Intelligence (2024).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data IEEE Transactions on Artificial Intelligence (2024)

Reference 2

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 3

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 4

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This paper cites : Internimage: Exploring large-scale vision foundation models with deformable convolutions.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data : Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 5

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This paper cites In: European Conference on Computer Vision (2014).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: European Conference on Computer Vision (2014)

Reference 6

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This paper cites International Journal of Computer Vision 127, 302–321 (2019) 20.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data International Journal of Computer Vision 127, 302–321 (2019) 20

Reference 7

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 8

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Reference 9

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This paper cites In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009)

Reference 10

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This paper cites : Panda-70m: Captioning 70m videos with multiple cross-modality teachers.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data : Panda-70m: Captioning 70m videos with multiple cross-modality teachers

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 12

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This paper cites Packt Publishing, Birmingham, UK (2023).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Packt Publishing, Birmingham, UK (2023)

Reference 13

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This paper cites International Journal of Computer Vision, 1–37 (2024).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data International Journal of Computer Vision, 1–37 (2024)

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This paper cites Computer Science Review 48, 100553 (2023).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Computer Science Review 48, 100553 (2023)

Reference 15

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 16

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This paper cites Computational intelligence and neuroscience 2020 (2020).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Computational intelligence and neuroscience 2020 (2020)

Reference 17

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This paper cites Advances in Neural Information Processing Systems 36 (2024).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Advances in Neural Information Processing Systems 36 (2024)

Reference 18

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This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence45(9), 10850–10869 (2023).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data IEEE Transactions on Pattern Analysis and Machine Intelligence45(9), 10850–10869 (2023)

Reference 19

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: 32nd USENIX Security Symposium (USENIX Security 23), pp

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 21

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp

Reference 22

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Unresolved cited work

Reference 23

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Conference on Robot Learning, pp

Reference 24

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This paper cites In: Field and Service Robotics, pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Field and Service Robotics, pp

Reference 25

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This paper cites In: 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp

Reference 26

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This paper cites In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp

Reference 27

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This paper cites Artificial intelligence review 56(9), 9221–9265 (2023).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Artificial intelligence review 56(9), 9221–9265 (2023)

Reference 28

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp

Reference 29

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Reference 30

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This paper cites IEEE Access 7, 22 64323–64350 (2019).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data IEEE Access 7, 22 64323–64350 (2019)

Reference 31

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Mathematical Problems in Engineering 2022 (2022)

Reference 32

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This paper cites Journal of Imaging 8(11), 310 (2022).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Journal of Imaging 8(11), 310 (2022)

Reference 33

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Image Data Augmentation for Deep Learning: A Survey

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Improving Adversarial Robustness of Ensembles with Diversity Training

Reference 35

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This paper cites Advances in neural information processing systems 30 (2017).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Advances in neural information processing systems 30 (2017)

Reference 36

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This paper cites Advances in neural information processing systems 29 (2016).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Advances in neural information processing systems 29 (2016)

Reference 37

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This paper cites In: Proceedings of the British Machine Vision Conference 2016, BMVC (2016).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the British Machine Vision Conference 2016, BMVC (2016)

Reference 38

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This paper cites In: 2022 26th International Conference on Pattern Recognition (ICPR), pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: 2022 26th International Conference on Pattern Recognition (ICPR), pp

Reference 39

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Computer Graphics Forum, vol

Reference 40

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This paper cites Automation in Construction 155, 105060 (2023).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Automation in Construction 155, 105060 (2023)

Reference 41

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This paper cites International Journal of Computer Vision129(1), 225–245 (2021).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data International Journal of Computer Vision129(1), 225–245 (2021)

Reference 42

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This paper cites In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp

Reference 43

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 44

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This paper cites In: International Conference on Machine Learning, pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: International Conference on Machine Learning, pp

Reference 45

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 46

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This paper cites Synthetic Data in AI: Challenges, Applications, and Ethical Implications.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Synthetic Data in AI: Challenges, Applications, and Ethical Implications

Reference 47

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 48

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This paper cites Information Sciences 586, 485–500 (2022).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Information Sciences 586, 485–500 (2022)

Reference 49

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This paper cites Medical Image Analysis 84, 102688 (2023).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Medical Image Analysis 84, 102688 (2023)

Reference 50

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 51

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Observation 4a8d09d1-d09b-4048-8061-81bceee4865b · outbound

This paper cites On Convergence and Stability of GANs.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data On Convergence and Stability of GANs

Reference 52

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This paper cites Advances in neural information processing systems 30 (2017).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Advances in neural information processing systems 30 (2017)

Reference 53

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This paper cites ACM Computing Surveys 56(4), 1–39 (2023).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data ACM Computing Surveys 56(4), 1–39 (2023)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.652317Z

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.615114Z

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This paper cites Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images

Reference 56

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This paper cites IEEE transactions on image processing 13(4), 600–612 (2004).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data IEEE transactions on image processing 13(4), 600–612 (2004)

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T20:46:57.580333Z

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This paper cites In: BIOSTEC (2), pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: BIOSTEC (2), pp

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.557060Z

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This paper cites ACM Journal of Data and Information Quality16(1), 1–33 (2024).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data ACM Journal of Data and Information Quality16(1), 1–33 (2024)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.521300Z

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This paper cites International Journal of Medical Informatics, 105413 (2024).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data International Journal of Medical Informatics, 105413 (2024)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.459737Z

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This paper cites Advances in neural information processing systems 31 (2018).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Advances in neural information processing systems 31 (2018)

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.407286Z

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This paper cites Computer vision and image understanding 179, 41–65 (2019).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Computer vision and image understanding 179, 41–65 (2019)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.378968Z

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

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T20:46:57.630042Z

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This paper cites A Note on the Inception Score.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data A Note on the Inception Score

Reference 64

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:46:58.340830Z

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This paper cites In: 2015 IEEE International Conference on Image Processing (ICIP), pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: 2015 IEEE International Conference on Image Processing (ICIP), pp

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.157790Z

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Observation da909c0b-c64e-45ea-af48-68461153e66f · outbound

This paper cites Electronics letters 44(13), 800–801 (2008).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Electronics letters 44(13), 800–801 (2008)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.122156Z

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Observation 74b08e7a-06a6-415b-bf44-8dd2e61cb602 · outbound

This paper cites Metrics to Quantify Global Consistency in Synthetic Medical Images.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Metrics to Quantify Global Consistency in Synthetic Medical Images

Reference 68

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T20:46:57.763776Z

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Observation 38f565ac-12cf-4eab-90b5-9af2b660e0cf · outbound

This paper cites an unresolved cited work.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:46:58.100268Z

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Observation 77e219d8-344c-40e6-89c9-75dff88cfb80 · outbound

This paper cites The annals of mathematical statistics 22(1), 79–86 (1951).

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data The annals of mathematical statistics 22(1), 79–86 (1951)

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T20:46:57.692486Z

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Unavailable: canonical work link unavailable.

Observation a857629e-2551-4373-aa2f-71210730fe9b · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:46:58.012988Z

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