Use Cases for Synthetic Data?Although generated by a computer algorithm, synthetic data represents real data accurately and reliably. Moreover, there are many use cases for synthetic data. However, its use is acutely felt as a substitute for sensitive data, especially in non-production environments for training, testing, and analysis. Some of the best use-cases of synthetic data are:TrainingThe possibility of having an accurate and reliable ML model depends on the data it is being trained on. And, developers depend on synthetic data when real-world training data is hard to come by. Since synthetic data increases the value of real-world data and removes non-samples (rare events or patterns), it helps increase AI models’ efficiency.TestingWhen data-driven testing is critical to the development and success of the ML model, synthetic data must be used. The reason being synthetic data is much easier to use and faster to procure than rule-based data. It is also scalable, reliable, and flexible.AnalysisSynthetic data is free from bias that is typically present in real-world data. It makes synthetic data a much-suited dataset for stress-testing AI models of rare events. It also analyses the data model behavior possible.Advantages of Synthetic DataData scientists are always looking for high-quality data that is reliable, balanced, free of bias and represents identifiable patterns. Some of the advantages of using synthetic data include:Synthetic data is easier to generate, less time-consuming to annotate, and more balanced.Since synthetic data supplements real-world data, it makes it easier to fill data gaps in real-worldIt is scalable, flexible, and ensures privacy or personal information protection.It is free from data duplications, bias, and inaccuracies.There is access to data related to edge cases or rare events.Data generation is faster, cheaper, and more accurate.Challenges of Synthetic DatasetsSimilar to any new data collection methodology, even synthetic data comes with challenges.The first major challenge is synthetic data doesn’t come with outliers. Although removed from datasets, these naturally occurring outliers present in real-world data help train the ML models accurately.The quality of synthetic data can vary throughout the dataset. Since the data is generated using seed or input data, synthetic data quality depends on the quality of seed data. If there is bias in the seed data, you can safely assume that there will be bias in the final data.Human annotators should check synthetic datasets thoroughly to ensure accuracy by using some quality control methods.Methods for Generating Synthetic Data
A reliable model that can mimic authentic dataset has to be developed to generate synthetic data. Then, depending on the data points present in the real dataset, it is possible to generate similar ones in the synthetic datasets.To do this, data scientists make use of neural networks capable of creating synthetic data points similar to the ones present in the original distribution. Some of how neural networks generate data are:Variational AutoencodersVariational autoencoders or VAEs take up an original distribution, convert it into latent distribution and transform it back into the original condition. This encoding and decoding process brings about a ‘reconstruction error’. These unsupervised data generative models are adept at learning the innate structure of data distribution and developing a complex model.Generative Adversarial NetworksUnlike variational autoencoders, an unsupervised model, generative adversarial networks, or GAN, is a supervised model used to develop highly realistic and detailed data representations. In this method, two neural networks are trained – one generator network will generate fake data points, and the other discriminator will try to identify real and fake data points.After several training rounds, the generator will become adept at generating completely believable and realistic fake data points that the discriminator won’t be able to identify. GAN works best when generating synthetic unstructured data. However, if it’s not constructed and trained by experts, it can generate fake data points of limited quantity.Neural Radiance FieldThis synthetic data generation method is used when creating new views of an existing partially seen 3D scene. Neural Radiance Field or NeRF algorithm analyses a set of images, determines focal data points in them, and interpolates and adds new viewpoints on the images. By looking at a static 3D image as a moving 5D scene, it predicts the entire content of each voxel. By being connected to the neural network, NeRF fills missing aspects of the image in a scene.Although NeRF is highly functional, it is slow to render and train and might generate low-quality unusable images.So, where can you get synthetic data?So far, only a few highly advanced training dataset providers have been able to deliver high-quality synthetic data. You can get access to open-source tools such as Synthetic Data Vault. However, if you want to acquire a highly-reliable dataset, Shaip is the right place to go, as they offer a wide range of training data and annotation services. Moreover, thanks to their experience and established quality parameters, they cater to a wide industry vertical and provide datasets for several ML projects.