Learning Model Relearning Triggered by Anomalous Inputs
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Solution Overview
Problem
The timing of relearning in learning devices is arbitrarily determined by users, leading to potential delays and inappropriate outputs if not performed promptly, as existing systems lack automatic anomaly detection and timely retraining mechanisms.
Innovation Solution
A learning assistance device with an assessment part that identifies anomalous inputs and triggers relearning using these inputs as additional data, enabling automatic and timely retraining without user intervention, thereby improving output accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If relearning timing is determined by user judgment, then the system allows flexible control, but relearning cannot be performed timely and appropriate outputs cannot be ensured
Solution Approach 1:
The learning assistance device automatically detects anomalous inputs and triggers relearning without requiring user judgment or intervention. The system serves itself by monitoring its own learning device performance, identifying when relearning is needed based on anomaly detection, and executing the relearning process autonomously, thereby eliminating delays caused by manual timing determination
Solution Approach 2:
The system implements a feedback mechanism where the learning assistance device continuously monitors inputs to the learning device, compares them against learned patterns, and detects anomalies. When anomalies are detected, the system feeds this information back to trigger automatic relearning, creating a closed-loop control system that ensures timely relearning based on actual performance needs rather than arbitrary user timing
2Reliability
If relearning is performed automatically upon anomaly detection, then output accuracy is improved, but the system requires additional components for anomaly assessment
Solution Approach 1:
The learning assistance device performs multiple functions within a single integrated system: it assesses anomalies in inputs, determines when relearning is needed, and executes the relearning process. By combining these functions in one multi-functional device rather than separate components, the system achieves automatic relearning while minimizing the increase in overall system complexity
Solution Approach 2:
The learning assistance device acts as an intermediary between the learning device and the relearning process. It receives inputs, assesses them for anomalies, and conditionally triggers relearning based on its assessment. This intermediary role allows the system to add anomaly detection capability without directly complicating the core learning device structure, as the assistance device handles the complex assessment logic separately
Data Source
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AI summary
A learning assistance device according to the disclosure is designed to perform relearning for a processing part including a learning device that has already undergone learning for performing a prescribed output from a prescribed input, the learning assistance device including: an assessment part for assessing an anomaly of the input on the basis of a prescribed reference; and a relearning part for, if the assessment part has assessed that the input is anomalous, carrying out relearning of the learner under a prescribed condition using the anomalous input as additional learning data.