Machine Learning Model Parameter Update via Secondary Inference
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Solution Overview
Problem
Conventional training of machine learning models, such as Neural Radiance Field (NeRF) models, is computationally expensive and time-consuming, requiring extensive resources and time to update parameters for new environments.
Innovation Solution
A system and method that utilize a second machine learning model to efficiently update the parameters of a first model, allowing it to adapt to new environments quickly, by processing the first model's parameters and input corresponding to the new context using the second model to generate updated parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional training is used to update machine learning model parameters for new environments, then the model can adapt to new contexts, but the process becomes computationally expensive and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-training a source model on source domain data and pre-processing target domain data before the actual adaptation. This preparation work is done in advance, so when adaptation is needed, the system can quickly update parameters using the pre-prepared components, significantly reducing the time required for model updates to new environments.
Solution Approach 2:
The system creates a copy of the source model parameters and uses this copy as the initial state for the target model. Instead of training from scratch or making significant modifications to the original model, the system copies the learned parameters and efficiently adapts them to the new environment, reducing both time and computational resources required for adaptation.
2Adaptability or versatility
If conventional training is used to update machine learning model parameters for new environments, then the model can adapt to new contexts, but computational resources are excessively consumed
Solution Approach 1:
The system extracts and separates the essential components needed for adaptation: source model parameters, target domain data, and a loss function. By extracting only the necessary elements and discarding unnecessary training overhead, the system achieves environment adaptation with minimal computational resource consumption, avoiding the expensive conventional training process.
Solution Approach 2:
The system changes parameters efficiently by updating model parameters through a simplified optimization process that leverages pre-trained source parameters and pre-processed target data. This parameter update approach requires significantly fewer computational resources compared to conventional full training, enabling resource-constrained systems to adapt models to new environments without excessive energy consumption.
3Productivity
If a system uses a second machine learning model to update the first model parameters, then the update process becomes faster and more efficient, but the system complexity increases
Solution Approach 1:
The system achieves universality by using a general framework that can work with any source model and target domain combination. The second machine learning model serves multiple functions: it acts as a parameter initializer, a loss calculator, and an adaptation guide. This multi-functionality reduces the need for specialized components for each adaptation scenario, managing system complexity while maintaining high update speed across different environments.
Data Source
AI summary
A device includes a memory configured to store a first model and a second model. The first model is configured to perform inference based on a first set of parameters corresponding to a first context. The device includes one or more processors configured to process, using the second model, the first set of parameters and input corresponding to a second context to generate an output of the second model. The one or more processors are also configured to update the first model to perform inference using an updated set of parameters based on the output of the second model.