Crowd-Sourced AI Image Processing via Model Merging
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Solution Overview
Problem
Users without sufficient resources or knowledge face challenges in deploying state-of-the-art image processing services due to the time and resource-intensive nature of training sophisticated machine learning models, especially when lacking sufficient labeled data for new classification tasks.
Innovation Solution
A crowd-sourced image recognition system that combines and integrates existing machine learning models trained for different tasks, allowing users to contribute models based on confidence levels and consistency, and attributes benefits to contributors, thereby reducing the need for extensive training data and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users train sophisticated machine learning models for image processing, then model accuracy and performance are improved, but training time and computational resources increase significantly
Solution Approach 1:
The system pre-trains multiple machine learning models on diverse datasets before they are needed. When a user requests image processing, the system selects from these pre-trained models rather than training a new model from scratch, thereby eliminating the time-consuming training phase while maintaining high accuracy.
Solution Approach 2:
The system combines multiple pre-trained models with different strengths and specialized knowledge. By merging these models, the system achieves comprehensive image processing capability without requiring each individual model to be trained on all possible scenarios, reducing overall training requirements while improving robustness.
2Reliability
If users train sophisticated machine learning models for image processing, then model accuracy and performance are improved, but computational resources and memory requirements increase significantly
Solution Approach 1:
The system merges multiple specialized pre-trained models that each require fewer resources individually. By combining their outputs rather than training one massive model, the system achieves high accuracy while keeping individual model resource requirements manageable and scalable.
Solution Approach 2:
The system segments the image processing task into multiple specialized sub-tasks, each handled by a dedicated pre-trained model. This segmentation allows each model to be smaller and more efficient, reducing overall computational resource requirements compared to a single comprehensive model.
3Ease of operation
If users deploy image processing services without sufficient resources or knowledge, then accessibility and ease of operation are improved, but the ability to produce satisfactory models deteriorates
Solution Approach 1:
The system automatically selects and combines appropriate pre-trained models based on the user's image processing needs without requiring the user to understand model training or selection. This self-service approach maintains high model quality through automated expert-level decisions while keeping the user interface simple and accessible.
Solution Approach 2:
The system provides a universal platform that handles multiple image processing tasks through a single interface. Users can access various image processing capabilities without needing specialized knowledge for each task, as the system automatically adapts the appropriate pre-trained models to the specific requirements.
4Reliability
If extensive training data is collected and used, then model accuracy is improved, but data storage requirements and processing time increase
Solution Approach 1:
The system performs model training in advance using diverse datasets, storing the trained models rather than the raw training data. This preliminary action eliminates the need to store and reprocess extensive training data while maintaining model accuracy, as the learned knowledge is already embedded in the pre-trained models.
Data Source
AI summary
Features related to systems and methods for generating a machine learning model that is a composite of at least two other models (e.g., crowd-sourced models contributed by users) are described. Each of the contributed models provide output values that may not be to scale. To account for these differences, a normalization factor for a first machine learning model is generated to adjust values produced by the first machine learning model to correspond with results from the second machine learning model. The crowd-sourced models along with the normalization factor are included in the new image model generated in the claims.