Synthetic Data

Artificial Intelligence calls for data, a lot of data. Yet training an AI model with real-world data is often tricky either because the data available must be kept private or gaining access to such data is expensive and requires a lengthy process, or simply because the data isn’t available.  Synthetic data generation is a game-changer

Synthetic data can be used to train AI models for scenarios for which limited data is available. Synthetic data provides the same probability distribution as real training data sets.

A neural network trained with both synthetic and real information is so accurate that it makes big data less relevant.

According to the analyst firm Gartner, “by 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated.” It adds, “The fact is you won’t be able to build high-quality, high-value AI models without synthetic data.”

To make your synthetic data project a catalyst for your business model innovation, NeuTigers products and consultants are here to guide you.

What is Synthetic Data Generation?

Learn how Synthetic Data replaces Big Data.

Professor Niraj K. Jha, is Professor of Electrical and Computer Engineering, Associate Chair at Princeton University, and Co-Founder at NeuTigers.

NeuTigers Synthetic Data Workflow

Neutigers Synthetic Data Generation

Our synthetic data generation toolkit relies on the TUTOR* Deep Neural Networks (DNNs) synthesis framework developed at Princeton University.

The data generated by NeuTigers synthetic data library has the same probability distribution as the real data. The framework validates the integrity of the generated data in real-time.

NeuTigers synthetic data’s framework also benefits from other developments from Princeton University. Mimicking how the human brain works, we optimize the neural networks in weights and architecture to reduce its model size while maintaining accuracy by combining the TUTOR DNN framework with the grow-and-prune paradigm.

It is similar to how a toddler interprets the world, and humans create energy-efficient “predictive models” that are very accurate, despite using very small data during the learning phase.

The human brain can carry out new tasks with limited experience. It utilizes prior learning experiences to adapt the solution strategy to new domains.

Unlike the human brain, deep neural networks (DNNs), on the other hand, generally need large amounts of data and computational resources for training.

This is where NeuTigers’ TUTOR DNN synthesis framework steps in. TUTOR targets non-image datasets for now. It synthesizes accurate DNN models with limited available data and reduced memory and computational requirements.

TUTOR produces synthetic data in three steps:

  • Drawing synthetic data from the same probability distribution as the training data and labeling the synthetic data based on a set of rules extracted from the real dataset.
  • Using two training schemes that combine synthetic data and training data to learn DNN weights.
  • Employing a grow-and-prune synthesis paradigm to learn both the weights and the architecture of the DNN to reduce model size while ensuring its accuracy.

NeuTigers synthetic data library reduces the need for labeled data by 5.9X and improves output accuracy by 3.4 percent despite using a smaller sample size during the learning phase.

Our technology uses fewer samples than Generative Adversarial Networks (GANs), a less effective tool for generating new synthetic data.

Learn more about Synthetic Data technology.

Read the academic paper on TUTOR Synthetic Libraries from Princeton University.