Score-Based Generative Model Latent Space Mapping
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Solution Overview
Problem
Score-based generative models (SGMs) are computationally expensive and limited to generating continuous data, making them inefficient for producing new data samples and unable to handle non-continuous data types such as graphs, molecules, text, or categorical data.
Innovation Solution
A technique involving a variational autoencoder (VAE) is used to convert data points into latent variable values, allowing the SGM to operate within a latent space and generate non-continuous data by leveraging mappings between latent variables and a base distribution, reducing the number of neural network evaluations and resource overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If SGM is used to generate new data samples, then data generation capability is achieved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent pre-computes and stores a mapping between the data distribution and a base distribution (e.g., standard normal distribution) during a training phase. This preliminary action allows the model to bypass expensive iterative neural network evaluations during actual data generation, achieving fast sampling by directly transforming base distribution samples through the pre-computed mapping.
Solution Approach 2:
The patent creates a simplified copy of the data generation process by learning a score function that approximates the true score of the data distribution. This copied score function can be evaluated efficiently without requiring the full complexity of the original data generation process, enabling fast generation while maintaining sample quality.
2Productivity
If SGM is used to generate new data samples, then data generation capability is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent performs computationally intensive operations during an offline training phase, pre-computing the score function and establishing the mapping between distributions. This shifts the computational burden from the online generation phase to the offline training phase, allowing resource-efficient sampling when actual data generation is needed.
Solution Approach 2:
The patent uses a simple base distribution (e.g., standard normal distribution) that is computationally cheap to sample from, and transforms these cheap samples into complex data samples through the learned score function. This approach replaces expensive direct sampling from the complex data distribution with cheap sampling from a simple base distribution.
3Difficulty of detecting and measuring
If SGM operates on continuous data, then derivative computation is possible, but applicability to non-continuous data is lost
Solution Approach 1:
The patent introduces a latent space as an intermediary between the input data and the score-based generative model. By mapping data to this latent space and performing operations there, the model can handle various data types including non-continuous data, while still enabling derivative computation in the continuous latent space where the SGM operates.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for training a generative model. The technique includes converting a first data point included in a training dataset into a first set of values associated with a base distribution for a score-based generative model. The technique also includes performing one or more denoising operations via the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The technique further includes performing one or more additional operations to convert the first set of latent variable values into a second data point. Finally, the technique includes computing one or more losses based on the first data point and the second data point and generating a trained generative model based on the one or more losses, wherein the trained generative model includes the score-based generative model.