Knowledge Transfer Between ML Model Architectures
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Solution Overview
Problem
The challenge in machine learning is replicating the performance of production ML models in pre-production environments due to architectural differences and the inability to move sensitive production data or models, which hinders testing and knowledge transfer.
Innovation Solution
A method and system that utilize knowledge distillation to transfer knowledge from a primary ML model with a first architecture to a secondary ML model with a different architecture by generating class probabilities and securely exporting the trained model to a pre-production environment using privacy protection techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge distillation is used to transfer knowledge between different ML model architectures, then model performance and adaptability are improved, but the complexity of the training process increases
Solution Approach 1:
The patent introduces an intermediary training process that uses generated labels as a bridge between the source ML model and the target ML model. This intermediary mechanism facilitates knowledge transfer across different architectures by creating a common training framework that reconciles architectural differences, thereby improving adaptability while managing training complexity through structured intermediate steps.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting training parameters such as learning rates, temperature scaling factors, and loss function weights during the knowledge distillation process. These parameter modifications enable effective knowledge transfer between models with different architectures by optimizing the training dynamics to accommodate architectural variations, thus improving model adaptability.
2Reliability
If privacy protection techniques are applied to securely export trained models to pre-production environments, then data security is improved, but the model export process becomes more complex
Solution Approach 1:
The patent applies preliminary action by performing model training and knowledge distillation in the production environment before exporting to pre-production environments. This preliminary training ensures that the model achieves optimal performance and security requirements are met before distribution, thereby enhancing data security while managing export complexity through advance preparation.
Solution Approach 2:
The patent uses copying by creating replicated instances of the trained model that can be securely exported to multiple pre-production environments. These copies maintain the security properties of the original model while enabling distributed testing and validation, thus improving data security through replication rather than direct model sharing.
Data Source
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AI summary
This disclosure relates to a method and system for managing knowledge of a primary ML model. The method includes generating a set of class probabilities for an unlabelled dataset based on a labelling function. The unlabelled dataset may be associated with the primary ML model, and the primary ML model may employ a first ML model architecture. Further, the method includes transferring the unlabelled dataset and the associated set of class probabilities for training a secondary ML model based on a knowledge transfer technique. The secondary ML model may employ a second ML model architecture. It should be noted that the first ML model architecture is different from the second ML model architecture.