Published by Unbox.ai
Machine learning models, especially deep neural networks, are trained using large amounts of data. However, for many machine learning use cases, real-world data sets do not exist or are prohibitively costly to buy and label. In such scenarios, synthetic data represents an appealing, less expensive, and scalable solution.
Additionally, several real-world machine learning problems suffer from class imbalance—that is, where the distribution of the categories of data is skewed, resulting in disproportionately fewer observations for one or more categories. Synthetic data can be used in such situations to balance out the underrepresented data and train models that generalize well in real-world settings.
Synthetic data is now increasingly used for various applications, such as computer vision, image recognition, speech recognition, and time-series data, among others. In this article, you will learn about synthetic data, its benefits, and how it is generated for different use cases.
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