Task-Aware Synthetic Data Generation Framework
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Solution Overview
Problem
Conventional approaches for generating synthetic training data for machine learning models are task-agnostic and do not account for downstream tasks or user-defined constraints, leading to limitations such as imbalanced datasets and bias.
Innovation Solution
A framework using a generative adversarial network (GAN) with a data value estimator and penalty computation layer to generate task-aware synthetic data that incorporates user-defined constraints like class balance and fairness, optimizing data samples to have high value for specific downstream tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional task-agnostic approaches are used to generate synthetic training data, then data generation is simple and fast, but the resulting datasets are imbalanced and biased, leading to poor model performance on specific downstream tasks
Solution Approach 1:
The data generation framework is segmented into distinct functional modules: a generator network that creates synthetic data instances, a data value estimator that evaluates task-specific value, and a penalty computation layer that enforces constraints. This segmentation allows each component to be optimized independently while working together to produce high-quality, balanced training data that improves downstream task performance.
Solution Approach 2:
The framework implements feedback loops where the data value estimator continuously evaluates generated synthetic data and provides guidance back to the generator. The penalty computation layer also provides feedback by computing constraint violations and adjusting generation parameters accordingly. This feedback mechanism enables iterative refinement of synthetic data quality and balance without requiring complex manual intervention.
2Adaptability or versatility
If synthetic data is generated without considering downstream tasks or user-defined constraints, then the generation process is efficient, but the data lacks task-specific value and exhibits bias
Solution Approach 1:
The framework dynamically adjusts generation parameters based on task-specific requirements and constraint conditions. The data value estimator evaluates synthetic instances using task-relevant criteria, and the generator modifies its parameters accordingly to produce data with higher task-specific value. This parameter adaptation enables the system to generate diverse, task-optimized data efficiently without exhaustive manual tuning.
Solution Approach 2:
The framework performs preliminary evaluation of synthetic data instances using the data value estimator before final selection for training. High-value instances are identified and selected in advance, ensuring that only task-relevant, balanced data is used for model training. This preliminary filtering action improves both task-specific adaptability and overall generation efficiency by avoiding waste on low-quality samples.
3Manufacturing precision
If conventional synthetic data generation methods are used, then computational resources are conserved, but the generated data does not maintain class balance or fairness
Solution Approach 1:
The framework applies partial action by focusing computational resources on generating and evaluating only the most valuable synthetic data instances for each class. Rather than exhaustively generating all possible samples, the data value estimator identifies and selects high-value instances that sufficiently represent each class, achieving class balance with reduced computational overhead.
Solution Approach 2:
The penalty computation layer dynamically adjusts generation parameters to enforce class balance and fairness constraints. When certain classes are underrepresented or exhibit bias, the system modifies generation parameters to increase diversity and balance in those classes. This adaptive parameter adjustment achieves manufacturing precision in data quality while managing computational resources efficiently through targeted corrections rather than complete regeneration.
Data Source
AI summary
Techniques for generating machine learning training data which corresponds to one or more downstream tasks are disclosed. In one example, a computer implemented method comprises generating one or more synthetic data instances for training a machine learning model, and determining a value of respective ones of the one or more synthetic data instances with respect to at least one task. One or more additional synthetic data instances for training the machine learning model are generated based at least in part on the values of the respective ones of the one or more synthetic data instances.