AI Model Uncertainty Measurement Using Class-Specific Perturbations
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Solution Overview
Problem
Existing AI models lack effective methods to quantify uncertainty and reliability, particularly in applications like image recognition and autonomous driving, leading to inconsistent performance and potential errors.
Innovation Solution
A method and apparatus that involve adding perturbations based on class importances to representative vectors of an AI model, measuring inference uncertainty through standard deviation of noise-added results, and terminating training when perturbations exceed thresholds, thereby enhancing model robustness and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate test sets are used to measure AI model performance, then measurement precision is improved, but loss of time and cost increase
Solution Approach 1:
The patent extracts the performance measurement process from the traditional separate test set approach and integrates it into the training process itself. By incorporating uncertainty measurement during training, the system eliminates the need for separate testing phases while maintaining measurement precision through the use of perturbation-based inference uncertainty calculation.
Solution Approach 2:
The patent merges the performance measurement function with the training process by simultaneously performing training and uncertainty measurement. The linear classifier generates both training updates and inference uncertainty measurements in parallel, combining two previously separate operations into one unified process that reduces time and computational resources.
2Productivity
If AI models are trained without uncertainty measurement, then training speed is improved, but reliability deteriorates
Solution Approach 1:
The patent performs preliminary uncertainty measurement actions during the training process itself. By calculating inference uncertainty using perturbed representative vectors while training is ongoing, the system proactively assesses model reliability before final deployment, ensuring both training speed and reliability are maintained without requiring post-training verification.
3Reliability
If perturbation-based uncertainty measurement is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by focusing perturbation-based uncertainty measurement specifically on the linear classifier's representative vectors rather than the entire neural network. By limiting the perturbation application to specific critical components (the representative vectors used for classification), the system achieves reliable uncertainty measurement without unnecessarily increasing overall device complexity.
Solution Approach 2:
The patent changes parameters by introducing controlled perturbations to the representative vectors. By systematically varying these vectors through added noise and measuring the resulting inference variations, the system transforms the complexity of uncertainty measurement into a manageable parameter-based approach that quantifies reliability through controlled parameter changes.
4Manufacturing precision
If class-specific perturbations are applied based on importance, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by differentiating perturbation strategies based on class importance. Critical classes receive higher perturbation magnitudes while less important classes receive lower magnitudes, allowing the system to focus computational resources on improving classification accuracy for the most important categories without uniformly increasing complexity across all classes.
Data Source
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.