Oct 7, 2021 · We propose a method that improves the generative adversarial network (GAN) with inverse cumulative distribution function for tabular data synthesis.
Oct 7, 2021 · We propose a method that improves the generative adversarial network (GAN) with inverse cumulative distribution function for tabular data ...
We propose a method that improves the generative adversarial network (GAN) with inverse cumulative distribution function for tabular data synthesis. This method ...
The paper covers the challenges and considerations involved in using deep generative models for tabular datasets, such as data normalization, ...
Designing a generative model to synthesize realistic tabular data is of great significance in data science. Existing tabular data generative models have ...
Jan 4, 2024 · In this paper, we present a framework that utilizes a GAN-based synthesizer to generate synthetic data that not only satisfies user-defined constraints.
May 31, 2024 · In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility ...
The DATGAN is a synthesizer for tabular data. It uses LSTM cells to generate synthetic data for continuous and categorical variable types.
Jan 8, 2024 · CTAB-GAN+ synthesizes privacy-preserving data with at least 21.9% higher machine learning utility (ie, F1-Score) across multiple datasets and learning tasks ...
Missing: inverse | Show results with:inverse
Abstract: The proposed method XPCA Gen, introduces a novel approach for synthetic tabular data generation by util- ising relevant patterns present in the ...