Latent Variable Model for Attribute-Based Data Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating data fitting predetermined labels are resource-inefficient and require large quantities of labeled training data, making them inefficient in terms of computing resources and labeling processes.
Innovation Solution
A computer-implemented method that trains a latent variable model using both labeled and unlabeled data, trains a classifier, and generates new data with predetermined labels by converting sampled data points from a latent space representation to a data representation using a trained decoder, allowing for efficient creation of labeled data without the need for prior label definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to generate data fitting predetermined labels, then data can be generated with desired labels, but large quantities of labeled training data are required and computing resources are wasted
Solution Approach 1:
The method performs preliminary actions by training the LVM and classifier on available labeled data before the actual data generation process. The latent space is pre-structured to encode label information, and the classifier is pre-trained to recognize label patterns. This preliminary preparation enables efficient generation of new labeled data without requiring large quantities of additional labeled training data during the generation phase.
2Measurement precision
If current methods are used to generate data fitting predetermined labels, then data can be generated with desired labels, but computing resources are wasted
Solution Approach 1:
The method performs preliminary actions by training the LVM and classifier on available labeled data before the actual data generation process. The latent space is pre-structured to encode label information, and the classifier is pre-trained to recognize label patterns. This preliminary preparation enables efficient generation of new labeled data without requiring large quantities of additional labeled training data during the generation phase.
Solution Approach 2:
The latent space serves as an intermediary representation that bridges the input data and the desired labeled output. By encoding data in the latent space where label information is structurally organized, the method enables efficient classification and generation without requiring extensive computing resources for direct processing of high-dimensional data. The latent space acts as a compressed, structured intermediate representation that facilitates resource-efficient label-based generation.
3Ease of operation
If labels are defined prior to training the LVM, then the generation process can be guided, but flexibility to discover new labels is lost
Solution Approach 1:
The method enables dynamic label definition by allowing labels to be specified after the LVM is trained rather than requiring fixed label definitions during training. The classifier can be trained on subsets of labeled data for specific attributes of interest, and new labels can be discovered on-the-fly by analyzing the latent space structure. This dynamic approach provides both guidance (through classifier constraints) and flexibility (through post-training label definition and latent space exploration).
Data Source
AI summary
A computer-implemented method according to one aspect includes training a latent variable model (LVM), utilizing labeled data and unlabeled data within a data set; training a classifier, utilizing the labeled data and associated labels within the data set; and generating new data having a predetermined set of labels, utilizing the trained LVM and the trained classifier.