Table Data Generation With Constraint Vectors and Attention
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Solution Overview
Problem
Conventional generative models struggle to effectively generate new data while constraining feature information unique to table-type data, particularly when dealing with both high-dimensional and low-dimensional information such as implicit semantic and sparse feature information.
Innovation Solution
A method and device that utilize a generator network, discriminator network, and constraint prediction network to generate new data by specifying constraints through a constraint vector variable, performing discrimination and prediction, and using MLP embeddings and multi-head attention networks to secure connectivity and calculate attention, while employing error measurement for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional generative models are used to generate new data, then data generation capability is achieved, but the ability to constrain feature information unique to table-type data is insufficient
Solution Approach 1:
The patent segments the data representation into two distinct components: high-dimensional latent vectors for general data distribution and low-dimensional constraint vectors for table-type feature constraints. This segmentation allows the generator to independently optimize each component, ensuring both data generation capability and constraint satisfaction without mutual interference.
Solution Approach 2:
The patent introduces an intermediary constraint vector that mediates between the high-dimensional latent space and the table-type data features. This constraint vector acts as a bridge, translating abstract constraints into actionable guidance for the generator while maintaining the independence of the latent space exploration.
2Adaptability or versatility
If only high-dimensional information is used for data generation, then generative capability is improved, but constraint satisfaction on low-dimensional feature information deteriorates
Solution Approach 1:
The patent adds another dimension to the generation process by introducing a separate low-dimensional constraint vector space parallel to the high-dimensional latent space. This dimensional addition allows the model to simultaneously operate in both high-dimensional generative space and low-dimensional constraint space, ensuring both adaptability and precision.
Solution Approach 2:
The patent changes the parameter representation by using different dimensionalities for different purposes: high-dimensional parameters for capturing data distribution complexity and low-dimensional parameters for enforcing table-type feature constraints. This parameter differentiation resolves the contradiction between versatility and precision.
3Manufacturing precision
If complex constraint mechanisms are added to ensure table-type data constraints, then constraint precision is improved, but model complexity increases
Solution Approach 1:
The patent extracts the constraint enforcement mechanism from the main generative process by using a separate discriminator component dedicated to constraint verification. This extraction allows constraint precision to be improved without increasing the complexity of the generator itself, as the constraint checking is handled by a specialized auxiliary component.
Solution Approach 2:
The patent implements a feedback mechanism where the discriminator evaluates constraint satisfaction and provides gradient feedback to guide the generator. This feedback loop enables precise constraint enforcement through iterative refinement without requiring complex hard constraints, maintaining model simplicity while achieving high precision.
Data Source
AI summary
A method and a device for generating data may generate new data while constraining feature information unique to table-type data. The method includes: generating a constraint vector variable specifying a constraint specific to the table-type data; acquiring generated data by applying the constraint vector variable and a latent vector variable to a generator; discriminating whether the generated data is real data or fake data by applying original data and the generated data to a discriminator; predicting whether the generated data satisfies the constraint specific to the table-type data; and generating a predicted constraint vector variable based on a prediction result.