Active Data Generation Using Prediction Error and Uncertainty
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Solution Overview
Problem
Existing data generation methods for AI are time-consuming, costly, and lack sufficient coverage of the desired data space, relying on human expertise without considering the reliability of predictions, particularly for 0/1 error measures.
Innovation Solution
A method involving training a first predictor, determining its prediction error and uncertainty, and using a second predictor to generate data points that maximize a combination of expected prediction error and uncertainty, ensuring optimal data space coverage and reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing data generation methods are used to maximize prediction error, then new data can be generated, but the method is time-consuming and costly
Solution Approach 1:
The system uses the predictor itself to identify which data points should be generated. The predictor automatically determines its own weaknesses by evaluating prediction errors and uncertainties, eliminating the need for external expert intervention in selecting training data points.
Solution Approach 2:
The method implements a feedback loop where the predictor's performance is continuously evaluated, and the identified weak points are used to generate new training data. This closed-loop system automatically improves the predictor by feeding back information about its own prediction errors and uncertainties.
2Reliability
If existing data generation methods are used, then data can be generated, but sufficient coverage of the desired data space cannot be guaranteed
Solution Approach 1:
Instead of uniformly sampling the data space, the method focuses computational resources on specific local regions where the predictor exhibits high uncertainty or large prediction errors. This targeted approach ensures that data generation occurs precisely where it is most needed to improve prediction reliability.
Solution Approach 2:
The data generation process is dynamic and adaptive, continuously adjusting which regions of the data space require new data points based on the predictor's current performance. As the predictor improves, the focus of data generation shifts to new challenging regions, ensuring comprehensive coverage over time.
3Reliability
If expert selection of data points is used, then data quality can be maintained, but manual work is required
Solution Approach 1:
The predictor automatically identifies its own training needs by evaluating its prediction errors and uncertainties, eliminating the need for expert intervention. The system serves itself by autonomously determining which data points would be most beneficial for training.
Solution Approach 2:
The uncertainty estimator acts as an intermediary between the predictor and the data generation process. It translates the predictor's performance into actionable insights about where new data should be generated, automating what previously required expert judgment.
4Measurement precision
If 0/1 error measures are used, then simple error classification is achieved, but the degree of error cannot be indicated
Solution Approach 1:
The method transitions from using simple 0/1 error classification to employing continuous uncertainty measures. This parameter change allows for nuanced differentiation of prediction quality, enabling the system to identify not just whether a prediction is wrong, but how uncertain the predictor is about its prediction.
Data Source
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AI summary
The invention relates to a method for generating data on the basis of data already available having individual annotated data points, the method comprising: - training a first predictor on the basis of data already available; - determining a prediction error of the first predictor for each data point; - training a second predictor to determine an anticipated prediction error of the first predictor and an uncertainty; - determining a data description which maximizes a combination of anticipated prediction error and uncertainty; and - generating data on the basis of the previously determined data description.