Synthetic Data Generation via Iterative User Labeling
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Solution Overview
Problem
Implementing a machine-learning model is time-consuming and challenging due to difficulties in obtaining sufficient and quality training data, as well as validating the model's accuracy.
Innovation Solution
A user-based labeling process is initiated where a user provides labels for data samples, and a machine-learning model is iteratively trained based on these labels. The model's accuracy is determined by comparing predicted labels to additional labeled sets, allowing for the generation of synthetic data once the model is deemed accurate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user-based labeling process is used to ensure data quality, then label correctness improves, but time consumption increases
Solution Approach 1:
The machine-learning model performs self-labeling by generating predicted labels for data samples without requiring continuous user intervention. The model iteratively improves its own labeling capability through feedback from user corrections, enabling autonomous operation after initial training.
Solution Approach 2:
The system performs preliminary user-based labeling to establish an initial trained model before automated operation. This preliminary action creates a foundation that enables subsequent automated labeling, reducing overall user time investment while maintaining quality standards.
2Measurement precision
If sufficient training data is collected to improve model accuracy, then model accuracy improves, but data accessibility decreases
Solution Approach 1:
The system generates synthetic data samples that copy the statistical properties and patterns of real data without requiring access to additional real-world data sources. These synthetic copies provide sufficient training material while maintaining data accessibility and privacy.
Solution Approach 2:
The system transforms existing data samples by applying parameter changes to generate varied training examples. By modifying attributes such as noise levels, transformations, and augmentations, the system creates diverse training data from limited sources.
3Measurement precision
If iterative training is performed to improve model quality, then model quality improves, but training time increases
Solution Approach 1:
The system implements feedback loops where user corrections to predicted labels are fed back into the training process. This targeted feedback allows the model to efficiently improve quality by focusing on specific error patterns rather than requiring extensive iterative retraining on all data.
Solution Approach 2:
The system performs partial retraining on selected data subsets rather than complete iterative retraining on all data. This selective approach achieves quality improvement with reduced training time by focusing computational resources on critical improvement areas.
Data Source
AI summary
Aspects described herein may relate to methods, systems, and apparatuses for labeling data in connection with synthetic data generation. The data labeling may begin with a manual process where a user provides labels for data. Based on the labels provided by the user, modified data may be generated and may include one or more encodings associated with the labels provided by the user. A machine-learning model may be trained to predict labels based on the modified data samples. Accuracy of the model may be determined based on comparing the predicted labels to further labels provided by the user and/or by allowing the user to indicate whether predicted labels are correct or incorrect. Once the model is determined to be accurate, the predicted labels may be used as a basis for generating synthetic data.