Neural Network Model Local Adaptation for Covariate Shift
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Solution Overview
Problem
Deep neural network-based machine learning models trained in centralized environments often fail to perform well in deployment environments with different characteristics due to covariate shift, and existing solutions face challenges in adapting to local conditions without extensive data transmission or computational resources.
Innovation Solution
A system comprising a model trainer and deployment destinations with local adaptation techniques, such as removing covariate shift, dynamic gating, and hybrid models, allows for iterative adaptation of neural network parameters using unsupervised learning and sensor data, enabling models to customize to specific environments without relying on central resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a deep neural network model is trained in a centralized environment using diverse training data, then the model achieves good generalization ability across multiple environments, but the model performs poorly when deployed in environments with different characteristics due to covariate shift
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model in a centralized environment using diverse training data from multiple sources and conditions. This initial training phase prepares the model with broad generalization capabilities before deployment, allowing it to adapt more effectively to specific local environments through subsequent fine-tuning with limited local data
Solution Approach 2:
The patent implements local quality by enabling each deployed model to adapt to its specific local environment through fine-tuning with local data. Each model develops environment-specific parameters while retaining the general knowledge from centralized training, creating locally optimized versions that maintain diversity and specialization for different deployment contexts
2Reliability
If a model is adapted to local deployment conditions using extensive data transmission and retraining, then the model performance improves in the specific environment, but the bandwidth requirements and computational resources increase significantly
Solution Approach 1:
The patent applies partial action by performing only the necessary portion of model adaptation locally at the deployment destination. Instead of complete retraining, the system fine-tunes specific model parameters using local data, achieving environment-specific optimization while minimizing data transmission and computational overhead by leveraging the pre-trained model's existing knowledge
Solution Approach 2:
The patent segments the model training process into two distinct phases: centralized pre-training that handles general pattern learning, and local fine-tuning that handles environment-specific adaptation. This segmentation allows the bulk of computational work to be done once centrally, with only minimal local computation and data transmission required for deployment-specific optimization
Data Source
AI summary
At a centralized model trainer, one or more neural network based models are trained using an input data set. At least a first set of parameters of a model is transmitted to a model deployment destination. Using a second input data set, one or more adaptive parameters for the model are determined at the model deployment destination. Using the adaptive parameters, one or more inferences are generated at the model deployment destination.