GAN Training Through Real-World Image Loss Compensation
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Solution Overview
Problem
Machine learning models face significant challenges in accurately modeling real-world losses due to environmental factors such as uncontrollable lighting and subpar printer quality, which complicate the training process and result in imperfect color representation and display issues.
Innovation Solution
Utilizing modified Generative Adversarial Networks (GANs) like Boundary Equilibrium GAN (BEGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) to train neural networks directly in real-world environments, accounting for display and environmental properties to optimize adversarial patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are trained in simulated environments, then training efficiency is improved, but real-world performance deteriorates due to unmodeled environmental losses
Solution Approach 1:
The patent introduces an intermediary device (e.g., display screen, printer) between the training environment and the real world. This intermediary physically reproduces the environmental losses (color shifts, brightness changes, resolution limits) that occur in real-world deployment. By training on images captured from this intermediary, the model learns to compensate for these losses without requiring complex environmental modeling.
2Reliability
If real-world environmental factors are fully modeled during training, then real-world performance is improved, but system complexity increases significantly
Solution Approach 1:
Instead of attempting to model complex environmental factors through software simulations, the patent creates a physical copy of the real-world display or printing process. This copy (the intermediary device) naturally introduces the same environmental losses as the real world, providing authentic training data without requiring complex environmental models. The complexity is shifted from software modeling to physical hardware.
3Productivity
If standard GANs are used for adversarial pattern generation, then generation speed is improved, but pattern accuracy in real-world conditions deteriorates
Solution Approach 1:
The patent performs preliminary action by capturing images from the intermediary device during the training phase. This pre-processing step incorporates real-world display/printing losses into the training data before the GAN generation process begins. When the GAN generates adversarial patterns later, they are already optimized for real-world conditions, improving accuracy without sacrificing generation speed.
Data Source
AI summary
In an approach to integrating real-world properties into machine learning training, a real-world image is received. The real-world image is compared to a simulated image, where the comparison is performed using a discriminator network of a generative adversarial network (GAN). A generator network of the GAN is trained with results of the comparison of the real-world image to the simulated image. Responsive to determining that the real-world image is not optimal, the real-world image is iteratively tuned, using the generator network of the GAN, until it is determined that the real-world image is optimal, where the real-world image is optimal if the real-world image meets a predetermined threshold for accuracy of one or more image parameters of the simulated image versus the real-world image. The discriminator network of the GAN is trained with the real-world image.