Uncertainty-Guided Test Data Creation for Neural Model Robustness
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Solution Overview
Problem
Existing supervised machine learning techniques lack a mechanism to efficiently verify and improve the robustness of learning models, particularly in terms of accurately distinguishing between correct and incorrect answers, and managing uncertainty and accuracy simultaneously.
Innovation Solution
A data-creation assistance apparatus and method that includes a storage device for neural network models and test data with labels, utilizing back propagation to specify uncertainty, generating minutely changed test data, and calculating deviations to identify changes that increase or decrease uncertainty, thereby improving model robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning model outputs ground truth for all test data to achieve high accuracy, then accuracy is improved, but uncertainty increases and robustness deteriorates
Solution Approach 1:
The patent segments test data into multiple groups based on uncertainty levels. By dividing the evaluation process into segments (high uncertainty vs. low uncertainty data), the system can identify which specific data points contribute to high overall uncertainty, allowing targeted improvements without sacrificing accuracy on well-understood data
Solution Approach 2:
The patent implements feedback by using uncertainty information to guide data creation and model improvement. The uncertainty calculation results feed back into the data creation process, allowing the system to iteratively improve robustness by focusing on high-uncertainty cases while maintaining accuracy on confident predictions
2Reliability
If extensive additional data is collected to improve model robustness, then reliability is improved, but loss of time and resources increases
Solution Approach 1:
The patent performs preliminary uncertainty analysis on existing test data before collecting additional data. By identifying high-uncertainty cases in advance using the existing dataset, the system can target data collection efforts specifically to those areas, avoiding unnecessary data collection and reducing time loss
Solution Approach 2:
The system uses its own existing test data and uncertainty calculations to identify where improvement is needed, rather than requiring external expert analysis or extensive manual data review. The uncertainty metric serves the dual purpose of both evaluation and guidance for improvement
3Reliability
If model parameters are adjusted to reduce uncertainty, then reliability is improved, but manufacturing precision of model output deteriorates
Solution Approach 1:
The patent applies different treatment to different parts of the data distribution based on local uncertainty characteristics. High-uncertainty regions receive targeted attention through additional data creation, while low-uncertainty regions maintain their existing high accuracy. This local differentiation prevents degradation of output accuracy on well-understood cases
Solution Approach 2:
The patent changes parameters (additional test data) rather than modifying model architecture or training parameters. By adding targeted test cases to the existing dataset, the system improves robustness through better evaluation coverage without altering the model's learned parameters that determine output accuracy
Data Source
AI summary
To efficiently verify and improve a robustness of a learning model for supervised machine learning. A data-creation assistance apparatus 100 includes: a storage device 101 configured to store a neural network model 110 and test data 120; and a computing device 104 configured to specify an uncertainty of an inference result acquired by the neural network model 110; acquire gradient information of the test data 120 by a back propagation process using the uncertainty as a loss; apply various minute changes to the test data 120 to generate a plurality of minutely changed test data, and calculate deviations between each of the plurality of pieces minutely changed test data and the test data 120; and specify, based on the uncertainty information, the gradient information, and the deviations, a minute change that increases or decreases the uncertainty.