Cell Image Learning Memory Validation to Prevent Training Failure
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Solution Overview
Problem
Existing machine learning systems for analyzing cell images fail to detect insufficient memory capacity, leading to abnormal endings without user awareness, which hinders effective training of learning models.
Innovation Solution
A memory capacity determination system and method that includes a selector to choose between training and validation modes, a determiner to assess memory sufficiency, and an informer to notify users of insufficient memory, allowing for proactive management of memory capacity during the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning is performed using a processor and memory without memory capacity validation, then training can proceed without interruption, but the system cannot detect insufficient memory capacity leading to abnormal endings
Solution Approach 1:
The patent applies preliminary action by executing validation mode training processes before actual training to detect potential memory capacity insufficiency. The system performs a preliminary check by running validation mode that validates memory capacity requirements, allowing users to identify and address insufficient memory capacity before initiating full training, thereby preventing abnormal endings during training.
Solution Approach 2:
The patent introduces an intermediary validation mode that acts as a mediator between system configuration and training execution. This validation mode serves as an intermediate step that assesses memory capacity requirements without performing full training, providing users with memory capacity information before actual training begins.
2Ease of operation
If validation mode is added to detect memory capacity insufficiency, then users can grasp insufficient memory capacity, but the operation process becomes more complex with mode selection
Solution Approach 1:
The patent applies universality by designing a training process that can operate in multiple modes (validation mode and training mode) using the same basic framework. The system provides multi-functionality where the same processing flow can validate memory capacity or perform actual training depending on the selected mode, reducing the need for separate dedicated validation systems.
Solution Approach 2:
The patent implements feedback by providing users with memory capacity determination results through the validation mode. The system feeds back information about whether memory capacity is sufficient or insufficient, allowing users to adjust their system configuration or understanding before proceeding with training, thereby improving ease of operation.
3Reliability
If memory capacity validation is performed before training, then abnormal endings can be prevented, but training time is increased due to additional validation processes
Solution Approach 1:
The patent applies partial action by having the validation mode perform only the necessary memory capacity validation steps without executing the complete training process. The validation mode performs a simplified version of training processes sufficient to detect memory capacity issues, avoiding the time cost of full training while still preventing abnormal endings.
Data Source
AI summary
A memory capacity determination system (200) in learning of cell images (80) includes a learning processor (10) including a first processor (10a) configured to execute processes of training a learning model (21), and a memory (10b); a selector (45) configured to select between a training mode of training the learning model, and a validation mode of validating whether a capacity of the memory becomes insufficient; and a determiner (12d) configured to determine whether the capacity of the memory becomes insufficient in the verification mode; and a display (121) configured to give a notice based on a first determination result (32).