Neural Network Parameter Estimation Using Proxy Training Subsets
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Solution Overview
Problem
Training neural networks with high-dimensional medical images is computationally expensive due to the significant memory and time requirements, making existing methods inefficient and costly.
Innovation Solution
Constructing a proxy dataset and using proxy networks to estimate parameters for neural networks by identifying regions of interest and assigning similarity scores, allowing for a more efficient and accurate calculation of hyper-parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full training dataset is used to estimate parameters for neural networks, then parameter estimation accuracy is improved, but computational cost and time requirements increase significantly
Solution Approach 1:
The training dataset is segmented into multiple subsets, each processed by a separate proxy network. This allows parallel processing of data segments, reducing overall computational time while maintaining comprehensive parameter estimation coverage through the aggregation of results from multiple proxy networks.
Solution Approach 2:
Proxy networks serve as intermediary models between the full training dataset and the main neural network parameter estimation process. These simplified proxy networks process data subsets and provide parameter estimates that approximate the results of processing the complete dataset, significantly reducing computational burden.
2Measurement precision
If full training dataset is used to estimate parameters for neural networks, then parameter estimation accuracy is improved, but memory requirements increase significantly
Solution Approach 1:
The training dataset is divided into manageable subsets that can be processed independently by separate proxy networks. This segmentation reduces the memory footprint required for each individual processing task, allowing the system to handle large datasets without requiring proportional increases in memory resources.
Solution Approach 2:
Instead of loading the entire training dataset into memory for parameter estimation, the method creates multiple copies of simplified proxy networks that each process a subset of the data. This approach trades model complexity for memory efficiency, enabling processing of large datasets with limited memory resources.
3Measurement precision
If full training dataset is used to estimate parameters for neural networks, then parameter estimation accuracy is improved, but computational resources increase significantly
Solution Approach 1:
The computational workload is segmented across multiple proxy networks that process different subsets of the training data in parallel. This distribution of computational tasks reduces the resource intensity of each individual processing unit and enables more efficient utilization of available computational resources through parallel execution.
Solution Approach 2:
Multiple simplified proxy networks are created as temporary, computationally inexpensive models to estimate parameters from data subsets. These proxy networks require minimal computational resources compared to the main neural network, allowing the system to perform comprehensive parameter estimation without prohibitive resource costs.
Data Source
AI summary
Apparatuses, systems, and techniques estimate parameters to train one or more neural networks based on uniqueuss of training data. In at least one embodiment, a subset of training data is selected and used to estimate parameters to train one or more neural networks, based on, for example, uniqueness of training data.