Neural Network Uncertainty Estimation via Segmented Learning Models
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Solution Overview
Problem
Neural network-based estimation methods struggle with providing interpretable outputs, making it difficult to diagnose faulty responses and understand the uncertainty associated with the estimates.
Innovation Solution
A computer-implemented estimation method that generates uncertainty associated with a final estimation produced by a neural network. This is achieved by using a first learning model to generate a feature vector and a first estimation, and a second learning model to generate at least one modeling parameter of a probability distribution, which is used to calculate the uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network is used for data fusion and estimation, then adaptability and generalization ability are improved, but interpretability of outputs deteriorates
Solution Approach 1:
The neural network is segmented into two distinct models: a first learning model that generates deterministic estimations and a second learning model that generates uncertainty measurements. This segmentation allows each model to specialize in one aspect (prediction vs. uncertainty quantification), thereby maintaining adaptability while improving interpretability through separate, specialized functions.
Solution Approach 2:
The second learning model acts as an intermediary that processes the outputs of the first learning model and transforms them into uncertainty measurements. This intermediary component bridges the gap between the black-box neural network and interpretable uncertainty information, allowing the system to maintain its adaptive capabilities while providing transparent uncertainty estimates.
2Adaptability or versatility
If a neural network is used for estimation, then generalization ability is improved, but ability to diagnose faulty responses deteriorates
Solution Approach 1:
The system implements feedback by using the uncertainty measurements from the second learning model to monitor and evaluate the reliability of predictions from the first learning model. When uncertainty exceeds thresholds, the system can trigger diagnostic procedures, data re-collection, or alternative processing paths, enabling effective detection and measurement of faulty responses while maintaining generalization ability.
3Loss of information
If uncertainty generation is added to the neural network, then interpretability is improved, but device complexity increases
Solution Approach 1:
The two learning models are merged into a unified neural network architecture that shares common components such as the encoder and feature extraction layers. This merging allows the system to generate both deterministic estimations and uncertainty measurements simultaneously without duplicating the entire network, thereby improving interpretability while controlling the increase in device complexity through shared computational resources.
Data Source
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AI summary
An estimation method is disclosed for generating an uncertainty associated with a final estimation produced by a neural network based on input data. The neural network comprises a first learning model comprising a first encoder configured to receive the input data and to generate a feature vector, and a decoder configured to receive the feature vector and to generate a first estimation relating to the input data, and a second learning model comprising a second encoder configured to receive the feature vector and the first estimation, and to generate at least one modeling parameter of a probability distribution, the second learning model being configured to generate said uncertainty based on said at least one modeling parameter. An apparatus implementing the estimation method for producing a signal, and a method for training the neural network are also disclosed.