Meta-DMoE AI Model Training via Multi-Source Meta-Distillation
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Solution Overview
Problem
Existing AI model training methods face challenges in domain adaptation due to domain shift, where models trained on one domain may not perform well on another, especially in real-world scenarios with privacy regulations and resource constraints, leading to inefficiencies and performance degradation.
Innovation Solution
The Meta-Distillation of Mixture-of-Experts (Meta-DMoE) method, which uses a transformer encoder for knowledge distillation across multiple domains, adapting a target AI model by combining outputs from multiple AI models and using soft pseudo-labels to minimize KL divergence, allowing for efficient domain generalization and adaptation without requiring access to raw private data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large AI model is trained on one domain, then the model achieves high accuracy on that domain, but the model performs poorly on other domains due to domain shift
Solution Approach 1:
The patent divides the training process into multiple segments, each handling a different domain. Multiple domain-specific AI models are trained separately on different domains, and their outputs are combined through a transformer encoder to achieve both domain-specific accuracy and cross-domain adaptability.
Solution Approach 2:
The patent creates a universal framework where multiple domain-specific models work together through a transformer encoder to handle multiple domains. The system achieves multi-functionality by allowing the same architecture to adapt to different domains while maintaining specialized knowledge in each domain.
2Adaptability or versatility
If multiple AI models are used to improve domain adaptation, then the generalization performance improves, but the computational resources and complexity increase
Solution Approach 1:
The patent merges multiple domain-specific models into a unified framework where their outputs are combined through a transformer encoder. This combining approach allows the system to leverage the strengths of multiple specialized models while presenting a cohesive interface, reducing the effective complexity of managing multiple independent systems.
Solution Approach 2:
The transformer encoder acts as an intermediary that combines the outputs of multiple domain-specific models. This mediator component manages the complexity by providing a standardized interface between specialized models and the final prediction, simplifying the overall system architecture.
3Use of energy by moving object
If knowledge distillation is used to compress models, then the model size and computational costs are reduced, but the training process becomes more complex requiring soft pseudo-labels and KL divergence
Solution Approach 1:
The patent uses knowledge distillation to create a compressed student model that copies the knowledge from larger teacher models. The student model learns from soft pseudo-labels generated by the teacher models, effectively copying their knowledge while using fewer computational resources during inference.
Solution Approach 2:
The patent changes the training parameters by introducing soft pseudo-labels and using KL divergence as the loss function. This parameter change enables the distillation process to transfer knowledge effectively from teacher to student models, achieving compression while maintaining performance.
4Measurement precision
If domain-specific models are trained separately, then each model achieves high accuracy on its domain, but privacy regulations prevent access to raw private data for centralized training
Solution Approach 1:
The patent segments the training process so that each domain-specific model is trained independently on its own domain data, which remains private and localized. This segmentation allows each model to achieve high domain accuracy while respecting privacy constraints, as the raw data never leaves its source domain.
Solution Approach 2:
The transformer encoder serves as an intermediary that combines the outputs of domain-specific models without requiring access to the raw private data. This mediator enables knowledge integration while maintaining data privacy, as it operates on model outputs rather than raw data.
Data Source
AI summary
A method has the steps of obtaining a set of training samples from one or more domains, using the set of training samples to query a plurality of artificial-intelligence (AI) models, combining the outputs of the queried AI models, and adapting a target AI model via knowledge distillation using the combined outputs.