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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.
Tingwei Shen,Ganning Zhao, andSuya 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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Tingwei Shen, Ganning Zhao, 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