How to obtain Synthetic Data?(02/28)
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How To Obtain Synthetic Data?
Synthetic Data is artificially generated data that mimics real data. It is particularly useful in fields where data is scarce or privacy protection is crucial. There are various methods for creating new data, but one of the most prominent methods involves the use of machine learning models.
GANs
Generative Adversarial Networks (GANs) are frequently used for data generation, capable of producing data that is hard to distinguish from real data. GANs learn the distribution of real data to generate new data, which is particularly effective for creating images, text, and audio data.
Diffusion Model
Diffusion models have gained attention as another method that excels in modeling complex data distributions. These models generate high-quality data through a process of gradually removing noise, and they are particularly powerful for high-dimensional data like images.
Augmentation
the Augmentation method involves creating new data by applying transformations to existing data. This method is mainly applied to image or audio data, artificially increasing the volume of data by applying various transformations to the existing data.
The primary goal of generating synthetic data is to ensure data diversity and solve the problem of data scarcity. It can also be used to protect personal information when dealing with sensitive data. However, when using synthetic data, it is crucial to carefully review whether the generated data accurately reflects the characteristics of real data and is free from bias and errors.
If you want to obtain high-quality synthetic data, learn more!