Dynamic Learning Parameter Switching for Image Capturing Bias
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Solution Overview
Problem
Machine learning-based image capturing systems face challenges in preventing learning bias, particularly in automatic image capturing scenarios where user preferences are not accurately represented due to biased learning data, leading to undesired image captures.
Innovation Solution
An information processing apparatus that dynamically switches between learning parameters based on predetermined conditions, using a primary learning parameter obtained from a broader data set and a secondary parameter from a more comprehensive data set to adjust and refine user preferences, thereby mitigating bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to learn user preferences for automatic image capturing, then image capturing accuracy can be improved, but learning bias occurs when learning data is not representative of actual user preferences
Solution Approach 1:
The system dynamically switches between multiple learning parameters (first learning parameter from first learning data group, second learning parameter from second learning data group) based on determination results. This dynamic adaptation allows the system to select the most appropriate learning model for current conditions, preventing bias by adjusting which data group influences the learning outcome.
Solution Approach 2:
The control circuit changes learning parameters based on predetermined conditions related to determination results. By switching between different learning parameters obtained from different learning data groups, the system adjusts its learning behavior to avoid bias while maintaining high capturing accuracy.
2Device complexity
If a single learning parameter is used for machine learning, then device complexity is reduced, but learning bias occurs and user preferences are not accurately represented
Solution Approach 1:
The system employs multiple learning parameters but manages complexity through dynamic switching based on determination results. The control circuit selects which learning parameter to apply based on predetermined conditions, maintaining manageable complexity while utilizing diverse learning data groups to prevent bias.
Solution Approach 2:
The control circuit acts as an intermediary that manages multiple learning parameters and determines which one to apply. This intermediary layer coordinates between the multiple learning data groups and the determining device, preventing bias while maintaining systematic control over the complexity of managing multiple learning parameters.
3Productivity
If automatic image capturing is performed at regular intervals, then productivity is improved, but user preferences are not accurately captured leading to undesired images
Solution Approach 1:
The system uses determination results as feedback to switch between learning parameters. The control circuit monitors whether captured images align with user preferences based on the determination device's assessment, and adjusts the learning parameter accordingly. This feedback mechanism ensures that automatic capturing at regular intervals continues to improve user preference accuracy over time.
Solution Approach 2:
The system dynamically adapts its learning parameter based on determination results while maintaining regular automatic capturing intervals. This dynamic adjustment allows the system to preserve high productivity through consistent capturing while improving user preference accuracy by selecting appropriate learning parameters based on real-time feedback.
Data Source
AI summary
An information processing apparatus includes a control circuit configured to set or transmit a first learning parameter to a determining device that performs processing based on a learning parameter. The control circuit sets or transmits a second learning parameter instead of the first learning parameter to the determining device in a case where a result of a determination made by the determining device satisfies a predetermined condition. The first learning parameter is a learning parameter that is obtained by performing machine learning using a first learning data group. The second learning parameter is a learning parameter that is obtained by performing machine learning using a second learning data group. The first learning data group encompasses the second learning data group and includes learning data that is not included in the second learning data group.