A Study on Improving Realism of Synthetic Data for Machine Learning

• URL: http://arxiv.org/abs/2304.12463v2
• Date: Fri, 28 Apr 2023 22:29:31 GMT
• Title: A Study on Improving Realism of Synthetic Data for Machine Learning
• Authors: Tingwei Shen, Ganning Zhao, Suya You
• Abstract summary: 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.
• Score: 6.806559012493756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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