cGAN Training With Embedded Graphical Encodings for Sparse Labels
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Solution Overview
Problem
Conditional Generative Adversarial Networks (cGANs) face challenges in training accuracy when labelled training data is sparse, hindering their performance in applications where data augmentation is necessary.
Innovation Solution
A method involving training a cGAN with a generator and discriminator, using a loss function that incorporates an error metric to measure differences in graphical encodings, and embedding calibration graphics to improve training accuracy, especially in scenarios with limited labelled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graphical encodings are embedded into images to enhance training accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges graphical encodings directly into the image data itself, combining the visual information with the encoded parameter representations. This integration allows the cGAN to process both types of information simultaneously without requiring separate data structures, thereby improving training accuracy while limiting the increase in complexity through unified data representation.
Solution Approach 2:
The graphical encodings act as an intermediary between the physical parameter values and the image data. These encodings serve as a bridge that translates numerical parameters into visual representations that the cGAN can process, enabling accurate training without requiring direct manipulation of complex parameter spaces.
2Manufacturing precision
If calibration graphics are added to images, then manufacturing precision improves, but loss of information increases
Solution Approach 1:
The calibration graphics are positioned at specific locations within the images rather than uniformly distributed. This localized placement allows the calibration information to be extracted without interfering with the entire image dataset, preserving the integrity of the original image information while providing precise calibration references for parameter measurement.
Data Source
AI summary
A method of training a conditional Generative Adversarial Network (cGAN) is disclosed. The cGAN has a generator and a discriminator. The method comprises obtaining a collection of images of components, each image having a physical parameter value relating to the component associated therewith, for each one of the collection of images of components, embedding a plurality of graphical encodings into the image that encode the associated physical parameter value, and training the cGAN using the collection of images of components with their embedded plurality of graphical encodings.