Synthetic Data Generation Algorithm

A Synthetic Data Generation Algorithm is a data generation algorithm that creates synthetic data records to solve a synthetic data generation task.

• AKA: Artificial Data Generation Method.
• Context:
  • It can include methods such as generative adversarial networks (GANs), variational autoencoders (VAEs), and differential privacy techniques.
  • It can be integrated into data generation systems to automate the creation of synthetic datasets.
  • It can be evaluated based on the quality, diversity, and privacy of the generated data.
  • ...
• Example(s):
  • One that uses GANs to generate synthetic images for training computer vision models.
  • One that employs VAEs to create synthetic patient data for healthcare research.
  • One that uses differential privacy to generate synthetic financial data for compliance purposes.
  • ...
• Counter-Example(s):
  • a Data Processing Algorithm.
  • a Data Cleaning Algorithm.
• See: Data Generation Task, Data Masking Algorithm, Generative Model.

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References

2023

• (Lu, Shen et al., 2023) ⇒ Yingzhou Lu, Minjie Shen, Huazheng Wang, Xiao Wang, Capucine van Rechem, and Wenqi Wei. (2023). “Machine Learning for Synthetic Data Generation: A Review.” In: arXiv preprint arXiv:2302.04062. doi:10.48550/arXiv.2302.04062
  • NOTE:
    • The paper explores different machine learning methods used for synthetic data generation, with a focus on neural network architectures and deep generative models like GANs and VAEs.
    • The paper outlines several general evaluation strategies for assessing the quality of synthetic data, including statistical difference evaluation and training on synthetic data with testing on real data (TSTR).
    • The paper identifies key challenges in synthetic data generation, such as the need for robust evaluation metrics and the potential biases in underlying models, which can affect the accuracy of the generated data.
    • The paper presents opportunities for future research, such as improving methods to detect and address biases in synthetic data and exploring new application domains for synthetic data generation.

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