18418197. TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS simplified abstract (Google LLC)
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 Unanswered Questions
- 1.11 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 18418197 titled 'TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS
Simplified Explanation
The method described in the patent application involves training neural networks to translate images between different domains. Here are some key points to explain the innovation:
- Obtaining source and target training datasets with images from different domains.
- Using a forward generator neural network to translate source images to target images.
- Using a backward generator neural network to translate target images back to source images.
- Training the forward and backward generators jointly to optimize an objective function.
Potential Applications
This technology could be applied in various fields such as image processing, computer vision, and artificial intelligence.
Problems Solved
This technology solves the problem of translating images between different domains efficiently and accurately.
Benefits
The benefits of this technology include improved image translation capabilities, enhanced performance of neural networks, and potential applications in real-world scenarios.
Potential Commercial Applications
Potential commercial applications of this technology include image editing software, content creation tools, and automated image processing systems.
Possible Prior Art
One possible prior art for this technology could be similar methods used in image-to-image translation tasks in the field of computer vision.
Unanswered Questions
How does the training process affect the performance of the neural networks over time?
The article does not provide information on how the training process impacts the performance of the neural networks as they learn to translate images between different domains.
Are there any limitations to the types of images that can be effectively translated using this method?
The article does not address any potential limitations or constraints on the types of images that can be successfully translated using this technology.
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.
