Pseudo Image Generation for Field-to-Simulation Domain Conversion
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Solution Overview
Problem
Existing machine learning systems face challenges in efficiently performing model learning due to the state difference between simulation and field environments, requiring extensive data preparation and labeling, and existing domain conversion methods lack accuracy in converting field images to simulation images.
Innovation Solution
A machine learning system utilizing a pseudo image generative model and multiple determination models to convert field images into simulation images, where the system learns to distinguish between real and generated images without extensive data labeling, improving conversion accuracy through competitive learning and feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain conversion method using generative model is used to convert field images to simulation images, then the state difference between simulation and field environments is reduced, but the conversion accuracy of the generative model needs improvement
Solution Approach 1:
The patent implements a feedback mechanism where the generative model's output is evaluated by a discriminator that provides feedback signals. The system uses adversarial training where the discriminator critiques the generated images, and this feedback is used to iteratively improve the generative model's conversion accuracy, resolving the contradiction between reliability and measurement precision.
Solution Approach 2:
The patent introduces a discriminator as an intermediary component between the generative model and the final output. This intermediary evaluates the converted images and provides guidance, acting as a mediator that helps improve the conversion accuracy without requiring direct manual intervention, thus resolving the accuracy precision contradiction.
2Measurement precision
If extensive data preparation and labeling is performed for model learning, then learning accuracy is improved, but the time and resources required increase significantly
Solution Approach 1:
The patent implements self-service through automated adversarial training where the system generates its own training data and automatically labels it through the discriminator's evaluation. The generative model and discriminator work together in a self-reinforcing loop that eliminates manual data preparation and labeling, achieving high learning accuracy without significant time investment.
Solution Approach 2:
The patent performs preliminary action by pre-training the generative model with available data and then using it to generate additional training samples before final model training. This preliminary generation of synthetic training data reduces the need for extensive manual data collection and labeling, saving time while maintaining learning accuracy.
Data Source
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AI summary
There is provided is a machine learning system that efficiently performs learning of a model that domain-converts an image. The machine learning system includes a first determination model that determines whether an input image is a second domain image, and a second determination model that determines whether an extracted image obtained by extracting an area where an object is presented from the input image is an extracted image obtained by extracting an area where an object is presented from the second domain image. Either a pseudo second domain image or the second domain image is selected and input into the first determination model, and either a first extracted image in the pseudo second domain image or a second extracted image in the second domain image is selected and input into an image extracting unit. Learning of the first determination model is performed based on a first determination result of the first determination model, learning of the second determination model is performed based on a second determination result of the second determination model, and learning of a pseudo image generative model, that coverts a first domain image obtained by capturing an image for an object into the pseudo second domain image, is performed based on the first determination result and the second determination result.