Vector Neural Network Feature Spectrum Evaluation
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Solution Overview
Problem
Existing machine learning models require specific training data sets for each purpose, making it necessary to prepare data sources for understood feature spectra, which can be cumbersome and inefficient, especially when evaluating target data with different types and purposes.
Innovation Solution
A method involving a vector neural network machine learning model that uses general-purpose training data to train the model, then calculates spectral similarity between reference and target feature spectra from specific layers, allowing for evaluation of target data without needing a specific training set for each purpose.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specific training data sets are prepared for each purpose, then evaluation accuracy for target data is improved, but data preparation complexity and time increase
Solution Approach 1:
The patent applies universality by training the machine learning model with general-purpose training data that can be used across multiple evaluation purposes. Instead of preparing separate training sets for each evaluation task, a single model trained on diverse general-purpose data can evaluate different types of target data (images, audio, text, etc.) by extracting feature spectra from intermediate layers, thereby reducing data preparation time while maintaining evaluation accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-training the machine learning model on general-purpose training data before actual evaluation tasks. This preliminary training establishes a robust feature extraction capability that can be reused for various evaluation purposes, eliminating the need for repeated data preparation and model training for each new evaluation task.
2Measurement precision
If specific training data sets are prepared for each purpose, then evaluation accuracy for target data is improved, but system complexity increases
Solution Approach 1:
The patent reduces system complexity by implementing a universal evaluation framework where a single machine learning model, trained on general-purpose data, can evaluate multiple types of target data. The key innovation is extracting feature spectra from intermediate layers of the model, which provides a unified approach for evaluating images, audio, text, and other data types without requiring separate processing pipelines for each purpose.
Solution Approach 2:
The patent applies extraction by obtaining feature spectra from intermediate layers of the machine learning model rather than using the full model output or requiring separate evaluation systems. This extraction approach simplifies the evaluation process by focusing on specific feature representations that capture essential characteristics of the target data, reducing the complexity of the overall system.
3Ease of manufacture
If general-purpose training data is used, then data preparation effort is reduced, but evaluation accuracy for specific target data may decrease
Solution Approach 1:
The patent replaces the mechanical approach of preparing specific training data for each evaluation task with a computational approach. By training on general-purpose data and then extracting feature spectra from intermediate layers, the system computationally adapts to specific target data types without requiring manual data preparation. This substitution maintains evaluation accuracy while significantly reducing data preparation effort.
Solution Approach 2:
The patent applies parameter changes by utilizing different intermediate layers of the machine learning model for different evaluation purposes. By selecting appropriate layers and adjusting the feature spectrum extraction parameters, the system can optimize evaluation accuracy for specific target data types while maintaining the benefit of using general-purpose training data.
Data Source
AI summary
An evaluation method for evaluating target data includes: inputting a plurality of training sets to a vector neural network machine learning model having a plurality of vector neuron layers to train the machine learning model, the training sets including of general-purpose training data having a type different from the target data and a label corresponding to the general-purpose training data; acquiring a reference feature spectrum; acquiring a target feature spectrum; calculating a spectral similarity that is a similarity between the reference feature spectrum and the target feature spectrum; and evaluating the target data using the spectral similarity.