Conversion Matrix Mapping for New ML Model Output Integration
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Solution Overview
Problem
Upgrading a machine learning model with a different output vector size is inefficient and time-consuming, as existing methods require extensive manual effort and lack clear category mappings.
Innovation Solution
A mapping function using a greedy algorithm generates a conversion matrix to translate the new model's output to the original model's format, enabling seamless integration without manual data conversion or extensive modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a new machine learning model with a different output vector size is introduced to improve model accuracy or efficiency, then the system can benefit from improved performance, but the integration process becomes extremely time-consuming and inefficient due to the need for manual mapping and adaptation
Solution Approach 1:
The system automatically generates the mapping between old and new model output vectors through greedy optimization without requiring manual intervention. The mapping generation module autonomously computes the correspondence relationships by optimizing the transformation, thereby eliminating time-consuming manual mapping processes while maintaining accurate integration between models of different output sizes
Solution Approach 2:
The system handles changes in output vector parameters (dimensions, sizes) between old and new models by dynamically generating adaptive mapping relationships. The greedy optimization process adjusts the mapping parameters based on the specific dimensional differences, allowing seamless integration regardless of the parameter changes in the new model
2Adaptability or versatility
If manual mapping methods are used to adapt the new model output to the existing system, then compatibility can be achieved, but the process requires extensive manual effort and is inefficient
Solution Approach 1:
The mapping generation module automatically performs the adaptation task by computing the mapping relationships through greedy optimization. This self-service mechanism eliminates the need for manual mapping operations, reducing operational complexity while ensuring accurate compatibility between the new model output and the existing system interface
Solution Approach 2:
The system introduces a mapping generation module as an intermediary component that bridges the new model and the existing system. This intermediary automatically computes and maintains the correspondence relationships, serving as a smart mediator that handles the adaptation complexity without requiring direct manual intervention
3Adaptability or versatility
If the output vector size of the new model differs from the original model (e.g., 1×M vs 1×N), then the new model can provide improved functionality, but adapting it to work with the existing system becomes problematic and time-consuming
Solution Approach 1:
The system dynamically adjusts the mapping parameters based on the dimensional differences between old and new model outputs. The greedy optimization process computes the appropriate transformation relationships for any vector size difference, allowing the system to handle various parameter changes without increasing complexity
Solution Approach 2:
The mapping generation module is designed to universally handle any output vector size difference between models. It provides a multi-functional solution that can adapt to different dimensional configurations (1×N, 1×M, or any other size differences) using the same greedy optimization framework, thereby managing complexity through universal adaptation
Data Source
AI summary
A computer-implemented method generates a conversion matrix for mapping an output of a second machine learning model to an expected output of a first machine learning model. A set of example data is iteratively fed to the first machine learning model to create a set of first outputs and to the second machine learning model to create a set of second outputs. For each output of the set of first outputs and a corresponding output of the set of second outputs, a matrix is generated based on an optimization technique that maps the output of the second machine learning model to the output of the first machine learning model. Each of the generated matrices is summed to create an initial conversion matrix. Finally, each column of the initial conversion matrix is normalized to generate the conversion matrix.