Neural Network Uncertainty Estimation via Measurement Network
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Solution Overview
Problem
Existing neural networks used in safety-critical applications, such as automated vehicles, lack efficient methods to monitor and quantify the uncertainty of their outputs in real-time, which is crucial for ensuring accuracy and safety.
Innovation Solution
A method is introduced to train a measurement network that predicts the uncertainty of task network outputs by using unlabeled training data and reusing intermediate results from the task network, allowing for rapid prediction of output distributions without altering the task network's behavior, thus enabling real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed error calculation is carried out to ascertain uncertainty of task network outputs, then measurement precision is improved, but computational effort and processing time increase significantly
Solution Approach 1:
The patent segments the uncertainty estimation task by separating the task network (which processes input data to produce outputs) from the measurement network (which estimates uncertainty). This segmentation allows each network to be optimized independently, with the measurement network being smaller and faster, thus reducing overall processing time while maintaining precision.
Solution Approach 2:
The patent introduces a measurement network as an intermediary component that estimates uncertainty without requiring complete re-processing of the entire task network. This intermediary approach allows rapid uncertainty assessment by using a simplified model that captures essential uncertainty characteristics without the computational burden of full error calculation.
2Measurement precision
If multiple modifications of the task network are used to generate multiple outputs for uncertainty estimation, then measurement precision is improved, but device complexity and computational resources increase
Solution Approach 1:
Instead of modifying the task network multiple times, the patent uses a single task network and a single measurement network. The measurement network copies and processes the outputs from the task network to estimate uncertainty, avoiding the need for multiple network instances or modifications. This reduces device complexity while maintaining estimation accuracy.
Solution Approach 2:
The measurement network is designed to be universal and can estimate uncertainty for any output from the task network without requiring specific modifications to the task network architecture. This multi-functional approach allows the same measurement network to handle various tasks and output types, reducing overall system complexity.
3Ease of manufacture
If unlabeled measurement data is used for training the measurement network, then ease of manufacture is improved, but measurement precision may be compromised
Solution Approach 1:
The measurement network uses unlabeled data from the task network outputs themselves as training data. The system essentially trains itself by extracting uncertainty patterns from the task network's own outputs, eliminating the need for external labeled data preparation while maintaining precision through self-generated training examples.
Solution Approach 2:
The task network outputs serve as an intermediary that bridges the gap between available data and training requirements. By using the task network's outputs (which are already computed) as training data for the measurement network, the system avoids the need for separate labeled data collection while maintaining training quality.
Data Source
AI summary
A method for training a measurement network which ascertains the uncertainty of an already trained task network. In the method: each training record of measurement data from a training data set is fed to a plurality of modifications of a deterministic task network, or fed multiple times to a probabilistic task network, and thus mapped onto a plurality of outputs; each training record is fed to the measurement network and mapped onto a prediction of the distribution of the plurality of outputs, wherein the processing chain of the measurement network includes a part of the processing chain of the task network; a predefined cost function evaluates the extent to which the prediction of the distribution is consistent with the outputs; and network parameters which characterize the behavior of that part of the measurement network that does not belong to the processing chain of the task network are optimized.