AI Model Perturbation for Uncertainty and Reliability Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI models lack effective methods to quantify uncertainty and reliability, leading to inconsistent performance and potential errors in classification tasks.
Innovation Solution
The method involves adding perturbations based on class importances to representative vectors of an AI model, using noise distributions to measure inference uncertainty and determine when to terminate training, thereby enhancing model robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are used for classification tasks, then classification capability is achieved, but uncertainty and reliability cannot be quantified
Solution Approach 1:
The patent applies preliminary action by adding perturbation noises to representative vectors before performing classification. This pre-processing step allows the model to evaluate multiple possible outcomes and quantify uncertainty in advance, rather than assessing reliability only after classification. The perturbation-based approach enables upfront assessment of model confidence and reliability metrics.
2Measurement precision
If standard AI classification is used, then classification speed is maintained, but performance measurement requires separate test sets
Solution Approach 1:
The patent implements universality by designing a system where the same AI model performs both classification and self-evaluation functions. By integrating perturbation-based uncertainty quantification into the classification process, the model simultaneously produces class predictions and reliability metrics using the same computational infrastructure, eliminating the need for separate test sets while maintaining measurement precision.
3Reliability
If perturbation noises are added to representative vectors, then inference uncertainty can be measured, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by systematically varying perturbation noise parameters (magnitude, distribution type, application method) to optimize the balance between uncertainty measurement accuracy and computational complexity. Different perturbation strategies are evaluated to find parameters that provide sufficient reliability information without excessive computational overhead, adapting the complexity level to specific application requirements.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A device for measuring performance of an artificial intelligence (AI) model includes: one or more processors and a memory; and the memory storing instructions configured to cause the one or more processors to perform a process including: determining perturbations for respective classes based on respective class importances and adding noises determined based on the respective perturbations to respective representative vectors of the respective classes; and generating an inference uncertainty of the AI model from inference results outputted by the AI model using a weight matrix including the noise-added representative vectors.