Energy Distance Feature Vector Similarity for Deep Learning Labeling
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Solution Overview
Problem
Conventional data labeling in deep learning applications is cumbersome and inefficient, particularly when dealing with large datasets such as images, audio recordings, and text files, as existing labeling models are difficult to train and generate.
Innovation Solution
A method and system for measuring similarities between sets of feature vectors using an Energy Distance measure, which generates feature representations from existing and target datasets, and calculates metric distances to facilitate efficient similarity measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional data labeling models are used, then data labeling can be automated, but the models are difficult to train and generate
Solution Approach 1:
The patent introduces an intermediary energy distance metric that mediates between the source dataset feature representations and target dataset feature representations. This energy distance serves as a bridge to assess domain compatibility and guide the labeling process, making the automated labeling more feasible by providing a measurable criterion for model training and generation.
Solution Approach 2:
The patent changes the parameter space by transforming data into feature representations using deep learning models and measuring similarities through energy distance calculations. This parameter transformation enables automated labeling by converting complex data into a standardized feature space where automated decision-making becomes feasible.
2Measurement precision
If manual data labeling is performed, then labeling accuracy can be maintained, but the process is cumbersome and time-consuming
Solution Approach 1:
The patent replaces the mechanical manual labeling process with an automated system that uses energy distance calculations to determine label assignments. This substitution eliminates the time-consuming manual process while maintaining accuracy through rigorous feature representation and distance-based decision-making.
Solution Approach 2:
The system performs self-service labeling by automatically assigning labels to data points based on their feature representations and energy distance measurements to labeled examples. This self-service capability eliminates the need for manual intervention while maintaining high labeling accuracy through intelligent automated decision-making.
3Quantity of substance
If existing labeling models are used for large datasets, then labeling can be performed, but the models are difficult to train and generate
Solution Approach 1:
The patent segments the large dataset into source datasets with known feature representations and target datasets requiring labeling. By dividing the data into these segments and using energy distance to bridge them, the system can handle large datasets more effectively, as the energy distance metric provides a scalable way to assess similarities across different data segments without requiring retraining of complex models.
Data Source
AI summary
A method, system and apparatus for measuring similarities between sets of feature vectors, including generating a feature representation from existing sources of datasets, generating representation of metric distances of the existing sources to each other using energy distance measure from the feature representation, generating feature representation of a target dataset, generating representation of the metric distances of each of the target dataset using the energy distance measure, and providing a choice of existing sources which further extremizes a geometric content of a hypervolume described by a pseudolabel sequence.