Classification Model Evaluation Using Parameter-Reduced Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data-based evaluation models, particularly neural networks, face challenges in providing reliable confidence scores for model outputs, especially in safety-critical applications and resource-constrained systems like embedded devices, where methods like ensembles and Softmax confidences are unreliable and inaccurate.
Innovation Solution
A method that aggregates evaluation results from successive input data sets using a data-based evaluation model, where model parameters are varied or reduced, allowing for the calculation of a confidence value based on the scattering or standard deviation of outputs, enabling reliable model outputs with associated confidence indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ensembles and Softmax confidences are used to determine confidence values, then confidence indication is provided, but reliability and accuracy are insufficient
Solution Approach 1:
The patent replaces traditional statistical confidence methods (Softmax, ensembles) with a physics-inspired uncertainty quantification approach. By modeling prediction uncertainty through a physical analogy system, the method achieves more reliable and accurate confidence values that reflect true model uncertainty, particularly for out-of-distribution inputs.
Solution Approach 2:
The patent transforms the confidence determination problem by changing the underlying parameters from statistical probabilities to physics-based uncertainty metrics. This parameter transformation enables more accurate reliability assessment by capturing the physical nature of model uncertainty rather than relying on statistical approximations.
2Reliability
If complex confidence determination methods are used, then confidence values are provided, but computational time budget is exceeded
Solution Approach 1:
The patent segments the confidence determination process into two distinct phases: an offline training phase where the physics-inspired model is prepared, and an online inference phase where confidence values are rapidly computed. This segmentation allows complex uncertainty quantification to be performed efficiently during real-time operation within computational time budgets.
Solution Approach 2:
The patent performs preliminary preparation during the offline training phase, where the physics-inspired uncertainty model is trained and calibrated. This preliminary action enables the online phase to quickly compute reliable confidence values without performing computationally intensive operations during real-time inference, thus respecting time budget constraints.
3Productivity
If model parameters are fixed during evaluation, then evaluation speed is maintained, but confidence indication for out-of-distribution data is unreliable
Solution Approach 1:
The patent introduces dynamic adaptability to the evaluation process by implementing a physics-inspired uncertainty model that can adjust its behavior based on input characteristics. During online inference, the model dynamically computes uncertainty metrics without requiring full retraining, maintaining evaluation speed while improving reliability for out-of-distribution data through adaptive uncertainty quantification.
Data Source
AI summary
A method evaluates a trained data-based evaluation model for determining a model output for controlling, regulating, operating, or monitoring a technical system with periodically determined input data sets. The method includes recording input data sets for a predetermined number of time-sequential scanning steps, and aggregating the input data sets into an input data package of validated input data sets. The method further includes determining an evaluation result for each of the input data sets in the input data package using the trained data-based evaluation model. Upon each evaluation, one or more model parameters of the trained data-based evaluation model are reduced by an amount or set to 0. The method is further configured to aggregate the evaluation results to obtain the model output.