Augmented-Image Training for Consistent Vector Image Generation
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Solution Overview
Problem
Neural network models for vector image generation often produce multiple correct answers due to the variability in expressing images, leading to inconsistent output.
Innovation Solution
A vector image generator model training method that includes generating augmented images through transformations of raw images and using a neural network model to output the original image, with self-supervised learning and labeled data to improve recognition and output reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network model is used to generate vector images, then image generation capability is improved, but output consistency deteriorates due to multiple correct answers
Solution Approach 1:
The patent introduces a feedback mechanism where the model receives feedback about whether its generated vector image matches the original image characteristics. This feedback loop enables the model to learn from its performance and adjust its outputs, thereby improving consistency while maintaining generation capability. The feedback is obtained by comparing generated images with original images and using this information to refine future generations.
Solution Approach 2:
The patent changes the training parameters and loss function parameters to emphasize image fidelity and consistency. By adjusting these parameters, the model is guided to produce outputs that are not only diverse in generation capability but also consistent in matching the original image characteristics, thus resolving the contradiction between versatility and reliability.
2Adaptability or versatility
If vector images are expressed in various methods, then creative flexibility is improved, but model determination accuracy deteriorates
Solution Approach 1:
The patent segments the image generation process into distinct components: original image input, vector image generation, and verification against original image characteristics. This segmentation allows the model to handle the complexity of multiple expression methods systematically, improving determination accuracy while preserving creative flexibility in how vectors represent images.
Solution Approach 2:
The feedback mechanism provides the model with information about whether its vector representation accurately captures the original image's essential characteristics. This enables the model to learn accurate determination while maintaining the flexibility to express images through various vector methods, as the feedback guides the model toward accuracy without restricting creative expression.
Data Source
AI summary
A vector image generator model training method includes receiving a vector image, transforming a raw image that is the received vector image, and generating an augmented image, and outputting the vector image by using a vector image generator model, based on the raw image and the augmented image. The vector image generator model includes a neural network model, and the vector image generator model is configured to output the raw image when the raw image and the augmented image are input.