2023 MachineLearningforSyntheticData
- (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
Subject Headings: Synthetic Data Generation, Synthetic Data Generation Method.
Notes
- The paper highlights that synthetic data generation addresses significant challenges in machine learning, such as data quality, scarcity, and privacy issues, by providing alternative datasets that mimic real-world data.
- The paper describes various application domains where synthetic data generation is impactful, including computer vision, natural language processing, speech, healthcare, and business.
- 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 emphasizes the importance of addressing privacy and fairness concerns in synthetic data generation, noting that sensitive information can be inferred from synthesized data.
- 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.
- The paper discusses the impact of synthetic data on regulatory compliance, particularly in fields like healthcare, where sharing real patient data is restricted due to privacy regulations.
- The paper showcases the effectiveness of synthetic data in improving machine learning models' performance by providing additional training data, thus mitigating issues related to data scarcity and enhancing the robustness of models.
Cited By
Quotes
Abstract
Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations. In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate. This paper presents a comprehensive systematic review of existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains. Additionally, it explores different machine learning methods, with particular emphasis on neural network architectures and deep generative models. The paper also addresses the crucial aspects of privacy and fairness concerns related to synthetic data generation. Furthermore, this study identifies the challenges and opportunities prevalent in this emerging field, shedding light on the potential avenues for future research. By delving into the intricacies of synthetic data generation, this paper aims to contribute to the advancement of knowledge and inspire further exploration in synthetic data generation.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 MachineLearningforSyntheticData | Huazheng Wang Yingzhou Lu Minjie Shen Xiao Wang Capucine van Rechem Wenqi Wei | Machine Learning for Synthetic Data Generation: A Review | 10.48550/arXiv.2302.04062 | 2023 |