Synthetic Data Generation Model Augmentation
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Solution Overview
Problem
Machine learning models often struggle with accuracy due to insufficient and non-diverse sample data sets, making it challenging to obtain large and varied datasets for training.
Innovation Solution
The use of synthetic data generation models to augment training data by generating synthetic data points that resemble task-specific data, leveraging general data sources to conserve task-specific data points and enhance model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If synthetic data generation models are used to augment training data, then the diversity and quantity of training data increase, but the complexity of the system increases
Solution Approach 1:
The patent uses synthetic data generation models to create copies of task-specific data points from general data sources. These synthetic copies resemble the original task-specific data while providing additional quantity and diversity, effectively multiplying the available training data without requiring collection of entirely new data points.
Solution Approach 2:
The patent introduces synthetic data generation models as intermediary components between general data sources and the machine learning model. These intermediaries transform general data into task-specific synthetic data points, facilitating the augmentation process while managing system complexity through a dedicated transformation layer.
2Adaptability or versatility
If synthetic data generation models are used to augment training data, then the diversity of training data increases, but the computational resources required increase
Solution Approach 1:
The patent employs parameter changes in the synthetic data generation models to control the diversity and characteristics of generated data. By adjusting model parameters, the system can generate diverse synthetic data points that match task-specific requirements while optimizing computational resource consumption based on the desired level of diversity.
3Reliability
If task-specific data points are used for training, then the model accuracy improves, but the data collection requirements increase
Solution Approach 1:
The patent creates synthetic copies of task-specific data points from general data sources. These copies maintain the essential characteristics needed for model accuracy while eliminating the need to collect and annotate large amounts of new task-specific data, thereby preserving accuracy requirements while simplifying data collection.
Solution Approach 2:
The system uses general data sources to automatically generate task-specific synthetic data points through the synthetic data generation models. This self-service approach eliminates the manual data collection and annotation process for task-specific data, as the system autonomously transforms general data into appropriate training examples.
Data Source
AI summary
Techniques are disclosed for augmenting data sets used for training machine learning models and for generating predictions by trained machine learning models. These techniques may increase a number and diversity of examples within an initial training dataset of sentences by extracting a subset of words from the existing training dataset of sentences. The techniques may conserve scarce sample data in few-shot situations by training a data generation model using general data obtained from a general data source.