Learning Data Generation System for Deep Learning Optimization
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Solution Overview
Problem
General users face difficulties in understanding and utilizing deep learning methods due to the complexity of determining the required learning data and resources needed for acquiring specific abilities, with deep learning requiring large datasets that are time-consuming and resource-intensive to prepare.
Innovation Solution
A method and system for automatically or semi-automatically generating learning data by specifying requirement information based on user requests, including details such as the learning subject, goal, and data format, which reduces the burden on users and optimizes resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning is performed based on a large quantity of data, then the applicable range of the acquired ability is widened, but the time and system resources required increase significantly
Solution Approach 1:
The system performs preliminary action by automatically generating appropriate learning data before the deep learning process begins. The learning data generation unit creates training datasets based on the learning subject and goal specifications, so that when learning is executed, the data is already prepared and optimized for the specific task, eliminating the need for users to manually collect and prepare large quantities of data.
Solution Approach 2:
The patent introduces an intermediary system consisting of the learning data generation unit and requirement information specification unit that mediates between the user's learning goals and the deep learning process. This intermediary automatically translates high-level learning objectives into specific data generation requirements, bridging the gap between user intent and the technical data preparation needed for effective deep learning.
2Adaptability or versatility
If deep learning is performed based on a large quantity of data, then the applicable range of the acquired ability is widened, but the system resources required increase significantly
Solution Approach 1:
The system performs preliminary action by automatically generating appropriate learning data before the deep learning process begins. The learning data generation unit creates training datasets based on the learning subject and goal specifications, so that when learning is executed, the data is already prepared and optimized for the specific task, eliminating the need for users to manually collect and prepare large quantities of data.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting the quantity and characteristics of generated learning data based on the specific learning goal and subject. The requirement information specification unit determines optimal data parameters (such as dataset size, diversity, and complexity) tailored to each learning task, avoiding the unnecessary consumption of system resources that would result from using excessively large datasets for all learning scenarios.
3Ease of operation
If general users attempt to prepare learning data themselves, then they can utilize deep learning methods, but the complexity and trouble required increases significantly
Solution Approach 1:
The system implements self-service by enabling the learning data generation unit to automatically generate training data without requiring user intervention in the complex data preparation process. Users simply specify their learning goals and subjects through the requirement information specification unit, and the system autonomously handles data generation, eliminating the need for users to understand or perform complex data preparation tasks.
Solution Approach 2:
The patent introduces an intermediary system consisting of the learning data generation unit and requirement information specification unit that mediates between the user's learning goals and the deep learning process. This intermediary automatically translates high-level learning objectives into specific data generation requirements, bridging the gap between user intent and the technical data preparation needed for effective deep learning.
4Loss of energy
If insufficient learning data is provided, then resource consumption is reduced, but the learning effectiveness and acquired ability deteriorate
Solution Approach 1:
The system applies parameter changes by dynamically adjusting the quantity and characteristics of generated learning data based on the specific learning goal and subject. The requirement information specification unit determines optimal data parameters (such as dataset size, diversity, and complexity) tailored to each learning task, avoiding the unnecessary consumption of system resources that would result from using excessively large datasets for all learning scenarios.
Solution Approach 2:
The system performs preliminary action by automatically generating appropriate learning data before the deep learning process begins. The learning data generation unit creates training datasets based on the learning subject and goal specifications, so that when learning is executed, the data is already prepared and optimized for the specific task, eliminating the need for users to manually collect and prepare large quantities of data.
Data Source
AI summary
The disclosure relates to a processing method for generating learning data, which may include: specifying requirement information for generating learning data, based on request information for making a request for learning; and transmitting the requirement information to a device that generates the learning data. The disclosure also relates to a system and a program that realize the method, and a storage medium that stores the program.