Our first proprietary model — zGAN — generates smart, synthetic outliers to optimize performance of classification-based machine learning models. Discover how zGAN generates synthetic data, capturing outliers for accurate, reliable results. See the process in action in our detailed scheme.

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zGAN is our breakthrough AI model designed for complex predictions and rare event forecasting. It generates synthetic "smart outliers" to fill in data gaps and enhances machine learning performance with greater prediction accuracy.

One of Lucid's key components is zGAN, a patented synthetic data generator that enables the modeling of complex scenarios and cases even with limited or incomplete data. zGAN generates high-quality synthetic data by simulating real-world conditions, making the model development process more flexible and robust.

Jan 22, 2025 · Powered by zypl’s proprietary technologies, including zGAN (a state-of-the-art synthetic data generator) and an integrated AutoML pipeline, Lucid makes AI accessible, efficient, and scalable for...

Oct 29, 2024 · Using zGAN, outliers can be generated in selected columns of datasets based on covariance matrices of real data derived from various customizable probability distributions with a specified limit on the distribution tails.

Oct 28, 2024 · This article provides a general overview and experimental investigation of the zGAN model architecture developed for the purpose of generating synthetic tabular data with outlier characteristics.

Oct 28, 2024 · This article provides a general overview and experimental investigation of the zGAN model architecture developed for the purpose of generating synthetic tabular data with outlier characteristics.

Sep 28, 2024 · Leveraging this new loss function, we propose ZGAN, a refined GAN model that guarantees a unique optimal discriminator and stabilizes the overall training dynamics. Furthermore, we demonstrate that optimizing ZGAN's generator objective minimizes a weighted total variation between the real and generated data distributions.

This article provides a general overview and experimental investigation of the zGAN model architecture developed for the purpose of generating synthetic tabular data with outlier characteristics.