Neural Network Prediction Reliability Using Neuron Ignition Differences
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Solution Overview
Problem
The existing evaluation methods for neural network learning models are not designed to handle situations where a neuron that did not ignite during learning may ignite during actual operation, leading to unintended behavior and decreased prediction accuracy, as they do not account for differences in ignition tendencies between learning and actual operation input data.
Innovation Solution
An evaluating device and method that acquire and utilize two indices: a first index indicating the difference in data space between learning and actual operation input data, and a second index indicating the difference in neuron ignition tendencies, to evaluate the reliability of prediction values from the learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the existing evaluation method (Euclidean distance and weighting) is used, then the evaluation process is simple, but the evaluation accuracy is insufficient when neurons ignite during actual operation that did not ignite during learning
Solution Approach 1:
The evaluation process is segmented into two distinct indices: a first index based on Euclidean distance for data space difference, and a second index based on neuron ignition tendency difference. This segmentation allows each index to capture specific aspects of reliability, improving overall evaluation accuracy while maintaining manageable complexity through modular computation.
Solution Approach 2:
The invention adds a new evaluation dimension by introducing the neuron ignition tendency index. Instead of relying solely on the traditional Euclidean distance metric, the system now evaluates both data space distance and neural network internal state changes (neuron ignition patterns), providing a more comprehensive reliability assessment from multiple dimensions.
2Reliability
If the evaluation considers only data space difference, then the evaluation process is simple, but it cannot detect when neurons that did not ignite during learning ignite during actual operation
Solution Approach 1:
The neuron ignition tendency index serves as an intermediary metric that bridges the gap between input data characteristics and output prediction reliability. By measuring how the activation patterns of neurons change between learning and actual operation phases, the system detects subtle shifts in model behavior that purely data-space metrics cannot capture, thereby improving prediction reliability assessment.
3Measurement precision
If the evaluation accuracy is improved by adding neuron ignition tendency assessment, then the reliability evaluation is more accurate, but the computational complexity increases
Solution Approach 1:
The system performs preliminary analysis by computing neuron ignition patterns during both the learning phase and actual operation phase, storing these patterns for later comparison. This preliminary action allows the evaluation to quickly compare pre-computed ignition tendencies rather than performing complex simulations during the actual evaluation, reducing evaluation time while maintaining high precision.
Data Source
AI summary
An evaluating device includes a first acquisition unit configured to acquire a first index, a second acquisition unit configured to acquire a second index, and an evaluating unit configured to evaluate reliability. The first index indicates the difference between learning input data and actual operation input data in data space. The second index indicates the difference in the ignition tendency of the neurons between the time of input of the learning input data in the learning model of the neural network and the time of input of the actual operation input data in the learning model of the neural network. The evaluating unit evaluates the reliability of the prediction value output from the learning model with respect to the actual operation input data based on the first index acquired by the first acquisition unit and the second index acquired by the second acquisition unit.