Learning Model Migration System for Cell Image Analysis
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Solution Overview
Problem
Existing techniques face challenges in easily migrating a learning model for cell image analysis from one learning device to another, especially due to differences in algorithm versions and licensing issues.
Innovation Solution
A migration system and method that includes a second learning device with a migration information input reception unit, a storage unit for the algorithm, a consistency determination unit, a notification unit, and a parameter setting unit. This system determines algorithm consistency and sets parameters accordingly to facilitate easy migration of the learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If only the first parameter is migrated to the second learning device while storing the same algorithm, then the migration process is simplified, but the first parameter may not be applied due to algorithm version differences requiring relearning
Solution Approach 1:
The patent implements a feedback mechanism where the second learning device sends algorithm version information back to the first learning device. This feedback loop enables the system to detect version differences and trigger appropriate actions (parameter remapping or relearning), thus resolving the contradiction between simplified migration and parameter compatibility.
Solution Approach 2:
The patent dynamically changes parameters based on algorithm version compatibility. When version differences are detected, the system remaps parameters to match the second algorithm's requirements, or triggers relearning if remapping is not feasible. This parameter adaptation mechanism ensures compatibility while maintaining migration simplicity.
2Adaptability or versatility
If the algorithm is duplicated from the first learning device to the second learning device, then the learning model can be migrated, but licensing restrictions may prevent direct duplication
Solution Approach 1:
The patent uses selective copying where only essential algorithm information (version identifiers and parameter mappings) is copied from the first learning device to the second, rather than duplicating the entire algorithm. This approach enables model portability while avoiding licensing issues and reducing the complexity of algorithm version management.
Solution Approach 2:
The patent segments the algorithm into distinct components: version information, parameter definitions, and parameter values. This segmentation allows the system to migrate only the necessary parameter information without duplicating the protected algorithm, thus improving model portability while simplifying version management.
3Reliability
If relearning is performed on the second learning device to ensure parameter compatibility, then parameter applicability is guaranteed, but the migration process becomes time-consuming and complex
Solution Approach 1:
The patent performs preliminary actions by checking algorithm version compatibility and preparing parameter remapping strategies before actual parameter migration. This preliminary assessment allows the system to avoid unnecessary relearning operations, thus ensuring parameter applicability while minimizing migration time.
Solution Approach 2:
The patent implements a partial action approach where only the minimal necessary operations (parameter remapping or selective relearning of specific parameters) are performed on the second learning device, rather than complete relearning. This reduces migration time while still ensuring parameter compatibility through targeted actions.
Data Source
AI summary
A migration system of a learning model for cell image analysis is a system that migrates a learning model from a first learning device to a second learning device, in which the second learning device includes an algorithm consistency determination unit that determines, based on second algorithm specification information and first algorithm specification information, whether or not consistency is established between a first algorithm and a second algorithm, and a learning model parameter setting unit that sets a first parameter to be used together with the second algorithm.