Neural Network Parameter Optimization Without Gradient Calculation
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Solution Overview
Problem
Existing object operating methods rely heavily on object libraries for processing success, leading to low flexibility and high computational resource requirements due to the need for gradient calculation in neural network training, which constrains the application of neural network models.
Innovation Solution
The method involves acquiring a collection of sample parameters, performing iteration processing to determine a target set of parameters by replacing sample parameters with those having the smallest loss value among pending sets, using forward propagation to optimize parameters without calculating gradients, thus reducing computational demands and improving training speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient calculation is used in neural network training, then model accuracy can be improved, but computational resource requirements increase significantly
Solution Approach 1:
The patent extracts and eliminates the gradient calculation component from the neural network training process. By removing this computationally intensive step while retaining forward propagation, the system achieves significant reduction in computational resource requirements while maintaining acceptable model performance for object processing tasks
Solution Approach 2:
The patent employs a simplified training approach that uses inexpensive computational methods (without gradient calculation) to train neural network models. This disposable-like approach to training allows rapid model development and deployment with minimal computational investment, suitable for applications where extreme precision is not critical
2Measurement precision
If traditional neural network training methods are used, then model performance can be optimized, but training speed decreases due to high computational demands
Solution Approach 1:
By extracting and removing the gradient calculation step from the training pipeline, the patent dramatically reduces training time. The forward propagation alone provides sufficient performance for many object processing tasks, enabling faster model iteration and deployment without requiring the computationally expensive backpropagation process
Solution Approach 2:
The patent applies partial training action by using only forward propagation without the full backpropagation gradient descent process. This partial approach is sufficient for achieving acceptable model performance in object processing applications, while significantly reducing training time and computational resources required
3Reliability
If object libraries are relied upon for object processing, then processing can be performed using traditional methods, but flexibility and adaptability are reduced
Solution Approach 1:
The patent substitutes the mechanical system of object libraries and traditional rule-based processing with a neural network model that learns patterns directly from data. This substitution enables the system to adapt to new object types and variations without requiring manual library updates, providing both reliability through learned patterns and flexibility through the neural network's inherent adaptability
Data Source
AI summary
Provided is an object operating method, includes: acquiring an object to be operated; inputting the object to be operated into a target model, wherein the target model is a trained neural network model and at least one set of parameters in the target model is acquired in a predetermined manner, and the target model is configured to carry out a recognition operation or a processing operation on the object to be operated; and acquiring an operation result output by the target model; wherein the predetermined manner includes: acquiring a collection of sample parameters corresponding to a first set of parameters of the target model, performing a plurality of iteration processing on the collection of sample parameters; acquiring a target set of parameters based on the collection of sample parameters subjected to the plurality of iteration processing; and determining the target set of parameters as the first set of parameters.