Survey on Synthetic Data Generation, Evaluation Methods and GANs
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References listed on IDEAS
- Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
- Sergey I. Nikolenko, 2021. "Synthetic Data Outside Computer Vision," Springer Optimization and Its Applications, in: Synthetic Data for Deep Learning, chapter 0, pages 217-226, Springer.
- Sergey I. Nikolenko, 2021. "Synthetic Data for Deep Learning," Springer Optimization and Its Applications, Springer, number 978-3-030-75178-4, July.
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- Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
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Keywords
synthetic data generation; generative adversarial networks; evaluation of synthetic data;All these keywords.
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