Synthetic Data Generation and Its Benefits

Synthetic data is artificially generated data that can be used as a substitute for real data when training machine learning models. It is a key part of AI development and can be critical to solving issues that can't be addressed with real data, such as confidentiality or privacy concerns.

The first step in generating synthetic data is to understand its purpose, then identify the type of data that needs to be generated. Then, the data can be generated using a variety of algorithms that can range from classical machine learning techniques to deep learning models such as decision trees.

Generative Models for Data Synthesis

One of the most common methods to generate synthetic data is to use generative models that take a series of training examples and generate data from them. These models typically make use of a variational autoencoder (VAE) or a generative adversarial network (GAN).

Variational autoencoders compress and transmit data between the encoder and the decoder to create an output that looks like the original dataset. This method is a very efficient and accurate way to generate data that closely resembles the real thing.

Generative adversarial networks or GANs have a generator and discriminator that are both trained to differentiate fake and real data. This is done by generating and testing a wide range of data points to determine which ones are actually fake and which ones are not. The generator can then be further trained to synthesize more realistic data items and the discriminator can be updated accordingly.

A few use cases for generating synthetic data are creating synthetic data sets for retraining algorithms whose performance has degraded, or for resolving a bias. These include creating synthetic data to correct a racial bias in crime/fraud detection, synthesizing home addresses and incorporating them into weather patterns for better risk prediction, and generating synthetic geodata that can be used as a substitute for physical locations in computer vision applications.

Time Series Data Synthesis

Time series data is a type of synthetic data that includes information about specific time periods. This is valuable to machine learning and AI algorithms as it allows them to learn patterns and predict the future. It is also useful for analyzing trends and anomalies. It can be created with a number of techniques, including autoregressive models and generative models such as a timeGAN.

In Computer Vision and Natural Language Processing, synthetic data can be used to train AI systems for tasks such as identifying objects, landmarks, faces, or even text. It can also be used to test and develop robotics systems that may not be able to perform well if they aren't able to accurately perceive their environment.

Fake It Till You Make It: Face Analysis in the Wild

A research project by Microsoft produced diverse human 3D faces that were labeled to be used as material for training models in computer vision and face parsing. The resulting models were able to match real data with high accuracy and can be used in applications such as facial recognition, landmark localization, or text understanding.