A study on improving realism of synthetic data for machine learning

Tingwei Shen; Ganning Zhao; Suya You

doi:10.1117/12.2664064

13 June 2023 A study on improving realism of synthetic data for machine learning

Tingwei Shen, Ganning Zhao, Suya You

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 1252910 (2023) https://doi.org/10.1117/12.2664064
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

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Abstract

Synthetic-to-real data translation using generative adversarial learning has achieved significant success in improving synthetic data. Yet, limited studies focus on deep evaluation and comparison of adversarial training on general-purpose synthetic data for machine learning. This work aims to train and evaluate a synthetic-to-real generative model that transforms the synthetic renderings into more realistic styles on general-purpose datasets conditioned with unlabeled real-world data. Extensive performance evaluation and comparison have been conducted through qualitative and quantitative metrics and a defined downstream perception task.

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Tingwei Shen, Ganning Zhao, and Suya You "A study on improving realism of synthetic data for machine learning", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 1252910 (13 June 2023); https://doi.org/10.1117/12.2664064

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