Google llc (20240160937). TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS simplified abstract
Contents
- 1 TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS
Organization Name
Inventor(s)
Tomas Jon Pfister of Foster City CA (US)
TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160937 titled 'TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS
Simplified Explanation
The patent application describes a method for training neural networks to translate images between different domains. The method involves using a forward generator neural network to translate source training images to target images, and a backward generator neural network to translate target training images back to source images. The forward and backward generators are trained jointly to optimize an objective function.
- The method involves obtaining source and target training datasets with multiple images each.
- For each source training image, the image is translated to a target image using the forward generator neural network.
- For each target training image, the image is translated back to a source image using the backward generator neural network.
- The forward and backward generator neural networks are trained together by adjusting their parameters to optimize an objective function.
Potential Applications
This technology could be applied in image-to-image translation tasks, such as style transfer, image colorization, and domain adaptation.
Problems Solved
This technology solves the problem of translating images between different domains without the need for paired training data. It also improves the quality of image translation by jointly training forward and backward generators.
Benefits
The benefits of this technology include improved image translation quality, reduced reliance on paired training data, and the ability to learn domain-specific features from unpaired datasets.
Potential Commercial Applications
Potential commercial applications of this technology include image editing software, content creation tools, and image enhancement applications.
Possible Prior Art
One possible prior art for this technology is CycleGAN, a method for unpaired image-to-image translation using cycle-consistent adversarial networks.
What are the limitations of this technology in real-world applications?
The limitations of this technology in real-world applications include the potential for artifacts in translated images, the need for large amounts of training data, and the computational resources required for training neural networks.
How does this technology compare to existing image translation methods?
This technology improves upon existing image translation methods by jointly training forward and backward generators, which allows for better translation quality and the ability to learn domain-specific features from unpaired datasets.
Original Abstract Submitted
a method includes obtaining a source training dataset that includes a plurality of source training images and obtaining a target training dataset that includes a plurality of target training images. for each source training image, the method includes translating, using the forward generator neural network g, the source training image to a respective translated target image according to current values of forward generator parameters. for each target training image, the method includes translating, using a backward generator neural network f, the target training image to a respective translated source image according to current values of backward generator parameters. the method also includes training the forward generator neural network g jointly with the backward generator neural network f by adjusting the current values of the forward generator parameters and the backward generator parameters to optimize an objective function.
