Learning Device Automatic Data Selection for Inference Models
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Solution Overview
Problem
The existing methods require manual selection of learning data for inference models, leading to inefficient learning processes due to the inclusion of unsuitable data sets, which prolongs the learning time.
Innovation Solution
A learning device and method that automatically selects suitable learning data by distinguishing between data with and without correct answers, using an information processing unit to output the appropriate data along with the inference model, thereby eliminating the need for manual data selection and enhancing learning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of learning data is performed, then learning data suitability can be controlled, but learning time increases and operation complexity increases
Solution Approach 1:
The system automatically selects learning data by comparing target data with learning data through an information processing unit, eliminating the need for manual selection while ensuring data suitability. The learning device self-determines which learning data is appropriate based on the target data characteristics.
Solution Approach 2:
The system pre-compares target data with learning data before actual learning begins, identifying suitable learning data in advance. This preliminary comparison and selection process ensures that only appropriate learning data is used, avoiding time waste during the learning process.
2Reliability
If manual selection of learning data is performed, then learning data suitability can be controlled, but operation complexity increases
Solution Approach 1:
The information processing unit automatically performs data comparison and selection without requiring manual intervention. The system self-determines learning data suitability by processing and comparing data characteristics, thereby reducing operational complexity while maintaining reliability.
Solution Approach 2:
The manual mechanical process of selecting learning data is replaced with an automated information processing system. The information processing unit uses computational methods to compare and select appropriate learning data, substituting human operation with automated processing.
3Quantity of substance
If unsuitable learning data is included, then data quantity increases, but learning efficiency decreases
Solution Approach 1:
The system extracts and identifies suitable learning data from the overall learning data set by comparing it with target data. The information processing unit separates appropriate learning data from unsuitable data, ensuring that only high-quality data is used for learning and improving overall learning efficiency.
Solution Approach 2:
The system applies different quality standards to different learning data based on their suitability for the specific learning task. By evaluating and selecting learning data locally according to their individual characteristics and relevance to target data, the system optimizes learning efficiency while maintaining appropriate data quantity.
Data Source
AI summary
The present technology relates to a learning device, a generation method, an inference device, an inference method, and a program that enable learning data suitable for learning to be selected without a manual operation and enable learning of an inference model to be efficiently performed by using the selected learning data.In a learning device according to one aspect of the present technology, on the basis of a learning data group including learning data having a correct answer and a processing target data group including processing target data for learning, the processing target data for learning having no correct answer and corresponding to data to be processed at the time of inference, the learning device selects, from the learning data group, the learning data suitable for learning of an inference model used at the time of inference, and outputs the selected learning data together with the inference model obtained by performing learning using the selected learning data. The present technology can be applied to a computer that performs learning of CNN.