Prediction Model Behavior Alignment via Similarity Feedback

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Solution Overview

Problem

Existing prediction models converted to lighter versions often exhibit different behaviors despite maintaining similar prediction performance, leading to inconsistent results, particularly in safety-critical applications like object recognition in vehicles.

Innovation Solution

An information processing method that calculates the similarity between prediction results from a first and a second prediction model, determines training data based on this similarity, and trains the second model using machine learning to align its behavior with the first model's behavior, ensuring consistent output.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If a prediction model is converted to a lighter version, then processing efficiency is improved, but prediction behavior consistency deteriorates

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidprediction behavior consistency

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The patent uses feedback by calculating the degree of similarity between prediction results of the first and second prediction models, and using this similarity information to determine training data for retraining the second model. This feedback loop ensures that the lighter model's behavior aligns with the original model while maintaining processing efficiency

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent changes training parameters by selecting training data based on the degree of similarity between prediction results. When similarity is low (indicating behavioral differences), data generated from processing the input data is selected for retraining, thereby adjusting the model parameters to improve behavior consistency

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If prediction model conversion is performed to maintain prediction performance, then accuracy is preserved, but individual prediction result consistency worsens

Engineering Contradiction:

Improveprediction performanceVSAvoidindividual prediction result consistency

Core Design Contradiction:

Measurement precisionVSManufacturing precision

Solution Approach 1:

The patent applies local quality by focusing retraining efforts on specific cases where prediction behavior differs. By calculating similarity degrees and selecting training data based on low-similarity cases, the patent locally improves individual prediction result consistency without affecting overall prediction performance

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent uses partial action by not retraining on all data, but only on selected training data where the degree of similarity indicates behavioral differences. This partial retraining approach efficiently addresses individual prediction inconsistencies while preserving overall model performance

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20220292371A1Information processing method, information processing system, and information processing device

Publication Date: 2022.09.15 PANASONIC INTELLECTUAL PROPERTY CORP OF AMERICA

AI summary

An information processing method includes: obtaining first data; calculating a first prediction result by inputting the first data into a first prediction model; calculating a second prediction result by inputting the first data into a second prediction model; calculating a degree of similarity between the first prediction result and the second prediction result; determining second data which is training data for machine learning, based on the degree of similarity; and training the second prediction model by machine learning using the second data.