Attention GAN for Synthetic Tabular Data Correlation
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Solution Overview
Problem
Existing synthetic data generation methods fail to accurately preserve pairwise correlations between variables in tabular data, which is crucial for training effective machine learning models, especially when dealing with imbalanced categorical data.
Innovation Solution
A method using a generative adversarial network (GAN) with an integrated attention model to generate synthetic tabular data, where the attention model ensures that all categories from discrete attributes are sampled evenly, accurately reproducing the real data distribution and preserving column-wise correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing synthetic data generation methods are used, then data privacy is improved and additional privacy protections are provided, but pairwise correlations between variables are not accurately preserved
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary component between the generator and discriminator in a GAN framework. This attention mechanism selectively focuses on relevant variable relationships when generating synthetic data, enabling the system to preserve pairwise correlations while maintaining data privacy. The attention weights act as mediators that guide the generation process to prioritize correlation preservation.
Solution Approach 2:
The patent modifies the standard GAN architecture by introducing attention-based parameter adjustments. The attention mechanism dynamically changes the parameters (weights) assigned to different variable relationships during the generation process, allowing the system to adaptively preserve pairwise correlations. This parameter change enables the generator to focus on maintaining correlation structures while creating privacy-protecting synthetic data.
2Reliability
If more tabular data is used to train the machine learning model, then model performance is improved, but data capture cost and privacy issues increase
Solution Approach 1:
The patent creates synthetic copies of real tabular data using a GAN with attention mechanism. These synthetic copies replicate the statistical properties, distributions, and pairwise correlations of the original data without containing actual sensitive information. The copying process generates unlimited training data that maintains model performance while eliminating data capture costs and privacy concerns.
Solution Approach 2:
The patent transforms real data parameters into synthetic data parameters through the GAN training process. The attention mechanism ensures that the transformation preserves critical parameters such as pairwise correlations and variable distributions, while changing the actual data values to synthetic representations that do not compromise privacy or incur capture costs.
3Reliability
If a standard GAN is used for synthetic data generation, then data privacy is protected, but categorical data distribution and correlations are not accurately reproduced
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary that specifically addresses categorical variable relationships. The attention weights compute relevance scores between categorical variables, enabling the generator to accurately reproduce categorical data distributions and their correlations. This intermediary component bridges the gap between privacy protection and categorical data fidelity.
Solution Approach 2:
The patent applies parameter changes specifically tailored for categorical data through the attention mechanism. The attention-based generator dynamically adjusts parameters to capture categorical variable distributions and inter-categorical correlations, improving the accuracy of categorical data reproduction while maintaining the privacy benefits of synthetic data generation.
Data Source
AI summary
A computing device generates synthetic tabular data. Until a convergence parameter value indicates that training of an attention generator model is complete, conditional vectors are defined; latent vectors are generated using a predefined noise distribution function; a forward propagation of an attention generator model that includes an attention model integrated with a conditional generator model is executed to generate output vectors; transformed observation vectors are selected; a forward propagation of a discriminator model is executed with the transformed observation vectors, the conditional vectors, and the output vectors to predict whether each transformed observation vector and each output vector is real or fake; a discriminator model loss value is computed based on the predictions; the discriminator model is updated using the discriminator model loss value; an attention generator model loss value is computed based on the predictions; and the attention generator model is updated using the attention generator model loss value.