Signal Classification Feedback Correction for Confident Wrong Predictions
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Solution Overview
Problem
Machine learning systems, particularly neural networks, often produce incorrect predictions while being confident, leading to inaccurate outputs in classification and regression tasks, especially in robotic applications where sensor data is processed.
Innovation Solution
A computer-implemented method that determines a feature representation of an input signal, predicts the deviation of an intermediate signal from a desired output, and iteratively adapts this signal to improve accuracy, using multiple neural networks to correct predictions before outputting the final signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems (neural networks) are used for classification and regression analysis, then prediction performance is improved, but wrong predictions are produced with high confidence
Solution Approach 1:
The patent implements a feedback mechanism where the predicted output is fed back into the system through the same neural network to generate a corrected output. This self-feedback loop allows the system to identify and correct confident but wrong predictions by comparing the original prediction with the corrected prediction, thereby improving both accuracy and reliability simultaneously
Solution Approach 2:
The patent introduces an intermediate correction mechanism that acts as a mediator between the original neural network prediction and the final output. This intermediary correction step processes the original prediction through the neural network again, generating a corrected output that filters out confident wrong predictions while preserving accurate ones
2Speed
If standard neural networks are used, then processing speed is maintained, but incorrect predictions are output with high confidence
Solution Approach 1:
The patent applies preliminary action by performing a correction pass through the neural network before finalizing the output. Instead of waiting for wrong predictions to be identified after output, the system proactively corrects predictions by feeding them back through the network, ensuring accuracy is improved without significant speed penalty
Data Source
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AI summary
Computer-implemented method for determining an output signal characterizing a classification and/or a regression result of an input signal comprising the steps of: a. Determining a feature representation characterizing the input signal; b. Determining an intermediate signal characterizing a classification and/or regression result of the feature representation; c. Predicting, based on the feature representation and the intermediate signal, a deviation of the intermediate signal from a desired output signal of the input signal; d. Adapting the intermediate signal according to the determined deviation thereby determining an adapted signal; e. Providing the adapted signal as output signal.