Designing a Synthetic Data Solution

Learn how to create and iterate great synthetic datasets

Download the ebook to:

  • Discover the 3 main principles of successfully generating synthetic data
  • Learn about using synthetic data to solve edge cases
  • Understand how to control bias with synthetic data

What are the core principles of creating synthetic data?

With standard data acquisition processes, data is gathered through expensive, slow, and operationally intensive processes. It doesn’t allow for quick iteration or to improve the data that is used to train.

With synthetic data, this equation can be turned upside down. You can define the environment, the objects, the lights, the camera and get perfect fully annotated datasets – without dependence on manual, error prone processes.

Leveraging Synthetic Data for Hands-On-Wheel Detection

As presented at CVPR 2022

Find out how combining real and synthetic data can lead to safer driving in autonomous vehicles.

  • Learn how using synthetic data, along with a very small amount of real examples, can boost performance relative to using the same amount of only-real data
  • See a complete iteration of a data-centric approach using the Datagen platform to generate a specific edge case that was lacking in the training dataset
  • Authored by Paul Yudkin, Eli Friedman, Orly Zvitia and Datagen’s CTO, Gil Elbaz

Example images from the synthetic dataset:

Solving Privacy Concerns With Synthetic Data

Can we maintain user privacy while meeting the need for more data?

The short answer is yes – with synthetic data. In this eBook, we outline the rise of privacy regulations and the use of synthetic data for addressing privacy concerns in machine learning.

Download the eBook to learn about:

  • The privacy challenges inherent in training AI models with real-world data
  • Synthetic data vs. anonymization techniques
  • How differential privacy guarantees privacy for synthetic data