Accuracy Estimation Program for Unlabeled Data
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Solution Overview
Problem
Existing methods for verifying the accuracy of learned models in real environments are unreliable due to changes in data properties, requiring labeled real data which is costly to prepare and time-consuming to label.
Innovation Solution
An accuracy estimation program that calculates an index indicating the difference between datasets using prediction models, allowing for the estimation of prediction accuracy for unlabeled real data by specifying a relationship between datasets and using an index-accuracy curve to estimate accuracy without labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If verification is performed using labeled real data, then accuracy estimation for real environment is improved, but work cost and time consumption increase significantly
Solution Approach 1:
The patent uses synthetic data that copies the essential characteristics of real data distributions to train models. This synthetic data is generated through simulations that replicate real-world conditions, allowing accurate accuracy estimation without requiring actual labeled real data, thus significantly reducing time consumption while maintaining measurement precision
Solution Approach 2:
The patent replaces expensive and time-consuming labeled real data with computationally generated synthetic data. This synthetic data can be generated on-demand through simulations, eliminating the need for costly data labeling processes while providing sufficient accuracy estimation capability
2Measurement precision
If verification is performed using labeled real data, then accuracy estimation for real environment is improved, but work cost increases significantly
Solution Approach 1:
The patent creates synthetic data that copies the statistical properties and distributions of real data through simulation models. This approach eliminates the need for expensive manual labeling processes while maintaining the quality needed for accurate accuracy estimation, significantly reducing work cost
Solution Approach 2:
The patent replaces the mechanical process of manual data labeling with computational simulation processes. By using computer-generated synthetic data instead of human-labeled data, the system eliminates labor costs associated with labeling while achieving the same accuracy estimation goals
3Productivity
If model is trained using learning data with specific properties, then model learning is improved, but reliability of verification for real data decreases due to environment changes
Solution Approach 1:
The patent systematically varies parameters in the synthetic data generation process to create datasets that cover different environmental conditions and data distributions. This allows the model to be trained on diverse synthetic scenarios that mirror real-world variability, improving both learning efficiency and verification reliability across different environments
Solution Approach 2:
The patent creates a universal verification framework using synthetic data that can assess model performance across multiple real-world scenarios. The synthetic data generation system is designed to replicate various environmental conditions, making the verification process universally applicable to different real-world contexts without requiring scenario-specific labeled data
Data Source
AI summary
A program for causing a computer to execute processing including: acquiring a plurality of datasets, each of which includes data values associated with a label, the data values having properties different for each dataset; calculating an index indicating a degree of a difference between first and second datasets by using a data value in the second dataset; calculating accuracy of a prediction result for the second dataset, predicted by a prediction model trained using the first dataset; specifying a relationship between the index and the accuracy of the prediction result from the prediction model, based on the index and the accuracy calculated for each of a plurality of combinations of the first and second datasets; and estimating accuracy of the prediction result from the prediction model for a third dataset including data values without labels based on the specified relationship and the index between the first and third datasets.