Regression Model Uncertainty Quantification via Segmented Architecture
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Solution Overview
Problem
Deep neural networks used for regression tasks cannot express predictive uncertainty directly, which is crucial in safety-critical domains where imprecise predictions may result in high costs.
Innovation Solution
A method to automatically quantify the uncertainty of a prediction provided by a trained regression model for measured sensor data or image data, by generating an uncertainty layer in the model based on training data and empirical variance, without altering the neural network weights or architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple neural networks are trained simultaneously with different initial weights to enable predictive uncertainty expression, then the capability to communicate uncertainty is improved, but computation resources are excessively consumed
Solution Approach 1:
The patent segments the neural network into a feature extractor model (all layers except the last one) and an uncertainty layer (the last layer). This segmentation allows the uncertainty quantification to be performed separately from the main prediction task, enabling resource-efficient uncertainty expression without requiring multiple full network training sessions. The feature extractor remains frozen while only the uncertainty layer is trained on training data with empirical variance.
Solution Approach 2:
The patent adds a new dimension to the neural network output by appending an uncertainty layer that produces empirical variance values alongside the prediction. This transforms the output from a single prediction value to a pair of (prediction, uncertainty), enabling uncertainty communication without requiring multiple networks. The uncertainty layer operates in a separate computational dimension that processes the same input features independently.
2Productivity
If an uncertainty layer is generated and trained separately on training data, then resource efficiency is improved, but the integration with the existing model requires additional processing steps
Solution Approach 1:
The patent performs preliminary action by training the feature extractor model on the training data before generating the uncertainty layer. The feature extractor is trained to completion first, and only after this preliminary training is the uncertainty layer generated and trained separately. This preliminary action ensures that the feature extractor is fully optimized before being used to support uncertainty quantification, improving overall resource efficiency.
Solution Approach 2:
The feature extractor model serves as an intermediary between the input data and the uncertainty layer. It processes the input data to generate latent representations that are then passed to the uncertainty layer for variance computation. This intermediary role allows the uncertainty layer to be trained separately without requiring retraining of the entire network, reducing integration complexity while maintaining resource efficiency.
Data Source
AI summary
A computer-implemented method for automatically quantifying an uncertainty of a prediction is provided by a trained regression model for measured sensor data or image data, including obtaining the trained regression model, training data which were applied to train the regression model, and an empirical variance determined by the regression model applying the training data as input data, generating an uncertainty layer in the trained regression model based on the training data, and the empirical variance, obtaining the measured sensor data or image data as input data, outputting a prediction by processing the input data in the trained regression model and outputting an uncertainty value of the prediction by processing the input data by a feature extractor model and subsequently by the uncertainty layer, wherein the feature extractor model comprises all but the last layers of the regression model.