Supervised VAE Latent Space Optimization for Data Generation
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Solution Overview
Problem
Generating optimal data samples for machine learned models is challenging due to undefined relationships between input and output data, high-dimensional input spaces, and the need for samples to resemble real-world data, with existing methods failing to address these issues simultaneously.
Innovation Solution
A supervised variational autoencoder (VAE) system is implemented to learn the distribution of input data and its relationship with the target outcome, using a two-stage process of learning and generation to optimize latent feature representations and generate samples that are similar to real-world data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If we model the relationship between input data and target outcome using existing methods, then we can achieve some prediction accuracy, but the method fails to handle high-dimensional input spaces and cannot generate samples consistent with real-world data distribution
Solution Approach 1:
The patent transforms the high-dimensional input space into a lower-dimensional latent feature space through the encoder model. This dimensionality reduction allows the system to handle complex high-dimensional data while maintaining prediction accuracy. The latent space serves as an intermediate representation that captures essential patterns without the complexity of the original high-dimensional space.
Solution Approach 2:
The patent introduces a latent feature space as an intermediary between the input data and target outcome. This latent space acts as a mediator that learns the underlying relationships without requiring direct modeling of the complex high-dimensional input space. The encoder-decoder architecture with value predictor operates in this intermediate space, enabling versatile handling of different input types.
2Productivity
If we generate optimal samples to achieve target outcomes, then we can improve model performance, but the generated samples may not resemble real-world data and lack consistency with real-world distribution
Solution Approach 1:
The patent employs feedback mechanisms through the decoder model that reconstructs input data from latent features. The reconstruction loss provides feedback to ensure generated samples maintain consistency with real-world data distribution. Additionally, the value predictor guides the optimization process with feedback on how well samples achieve target outcomes, balancing performance improvement with distributional consistency.
Solution Approach 2:
The patent performs preliminary learning of the real-world data distribution during the training phase before generating optimal samples. The encoder and decoder are trained to capture the underlying data manifold and distribution characteristics. This preliminary action ensures that when optimal samples are generated later, they inherently respect the real-world distribution while achieving improved target outcomes.
3Ease of operation
If we use straightforward optimization in the input data space, then we can directly optimize for target outcomes, but the high-dimensional nature of the input space makes optimization infeasible and difficult to constrain
Solution Approach 1:
The patent performs optimization in the lower-dimensional latent feature space rather than the high-dimensional input space. This dimensionality change makes optimization computationally feasible and allows for easier constraint imposition. The latent space has fewer dimensions and more favorable geometric properties for optimization algorithms, transforming an infeasible problem into a tractable one.
Solution Approach 2:
The patent changes the optimization parameters from the original high-dimensional input features to the lower-dimensional latent features. This parameter transformation simplifies the optimization landscape, reduces computational complexity, and enables the use of standard optimization techniques that would be inapplicable in the original high-dimensional space.
Data Source
AI summary
A model learning and sample value generating framework includes a system and method to comprehensively integrate encoding, decoding and value predicting, and optimizing functions to reconstruct as accurate as possible an original input sample data space. The system leverages a variational autoencoder model to generate as realistic samples of that data space as possible. The system learns a value prediction function to achieve a target outcome based on the latent feature data instead of the original input data. Further, the system solves the optimization problem in the latent space without constraints to avoid the difficulty in optimizing in the original sample data space. The generated optimal samples are as similar as possible to the real-world input samples. The system provides a flexible data generation mechanism which is suitable for various kinds of target outcome specifications.