Synthetic images are used extensively for purposes like training self-driving cars, while synthetic audio and video data is used for applications including speech recognition, virtual assistants, and digital avatars. Synthetic text finds its use in applications like language translation, content moderation, and product reviews. Synthetic media including images, audio, and video.Popular types of synthetic data, classified according to the data type, include the following: Fully synthetic data sets are used in domains like finance and healthcare, where privacy and compliance concerns restrict the use of original data. Partially synthetic data finds its application in use cases where sensitive data needs to be replaced in the original training data set. Fully synthetic data, where the entire training data set consists of synthetic data.Partially synthetic data, where only a specific set of the training data is generated artificially.Generally, it falls into one of two categories: Synthetic data can be classified into different types based on their usage and the data format. In the following sections, you’ll learn more about the different types of synthetic data and then explore some techniques for generating it. The choice of methods for synthetic data generation depends on the type of data to be generated, with statistical methods being more common for numerical data and deep learning methods being commonly used for unstructured data like images, text, audio, and video. Deep neural network–based methods such as variational autoencoders and generative adversarial networks.Statistical approaches based on sampling from the source data distribution.There are several standard approaches for generating synthetic data. Generating synthetic data for machine learning Synthetic data generation has been successfully used to generate parallel training data for training deep learning models for neural machine translation. It helps avoid the key bottleneck in the machine learning lifecycle of the unavailability of data and allows teams to continue developing and iterating on innovative data products.įor example, building products related to natural language processing (NLP), like search or language translation, is often problematic for low-resource languages. Several industries-like consumer tech, finance, healthcare, manufacturing, security, automotive, and robotics-are already benefiting from the use of synthetic data. For data-hungry deep learning models, the availability of large training data sets is a massive bottleneck that can often be solved with synthetic data.Īdditionally, synthetic data can be used for myriad business problems where real-world data sets are missing or underrepresented. It’s generated with algorithms as well as machine learning models to have similar statistical properties as the real-world data sets. Synthetic data is a form of data augmentation that is commonly used to address overfitting deep learning models.
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