Domain Transformation Neural Network for Image Translation
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Solution Overview
Problem
Current image-to-image translation technologies face challenges in effectively transforming images from a source domain to a target domain, especially when there is a significant difference in low-level image statistics such as noise, resolution, and illumination, and when labeled examples from the target domain are scarce.
Innovation Solution
A domain transformation neural network is implemented, which receives an input image and a noise vector to generate a transformed image by using a combining sub-neural network to concatenate the image with a noise-generated channel and a convolutional sub-neural network applying resolution-preserving convolutions, along with a training method involving a discriminator and task neural network to optimize the transformation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image-to-image translation methods are used, then transformation from source domain to target domain is attempted, but the method fails to effectively handle significant differences in low-level image statistics such as noise, resolution, and illumination
Solution Approach 1:
The patent applies parameter changes by transforming image statistics through learned mappings. The neural network learns to map source domain image statistics (noise, resolution, illumination parameters) to target domain statistics by optimizing transformation parameters during training with labeled target domain images, enabling effective handling of significant differences in low-level image statistics
Solution Approach 2:
The patent uses an intermediary approach by introducing a target domain adaptation layer that acts as a mediator between source domain images and the final target domain output. This intermediary component learns the statistical mapping from source to target domain using labeled examples, bridging the gap caused by significant differences in noise, resolution, and illumination characteristics
2Quantity of substance
If labeled examples from target domain are scarce, then traditional training methods struggle, but the patent leverages labeled examples from source domain to generate labeled target domain images
Solution Approach 1:
The patent applies copying by using labeled examples from the source domain as proxies for target domain labels. The method copies the label information from source domain images and applies it to transformed target domain images, enabling training without requiring scarce target domain labels. This copying approach allows the system to leverage abundant source domain annotations effectively
Solution Approach 2:
The patent achieves universality by creating a domain adaptation framework that can work with any source-target domain pair given labeled source examples. The learned transformation model is universal and can be applied to generate labeled target domain images for multiple different tasks and domain combinations, making the system adaptable beyond a single specific application
3Reliability
If a domain transformation neural network is implemented, then effective adaptation of source domain images to target domain is achieved, but the network requires complex architecture including combining sub-neural network, convolutional sub-neural network, and discriminator
Solution Approach 1:
The patent applies segmentation by dividing the domain transformation network into distinct functional modules: a combining sub-neural network that integrates source images with learned domain characteristics, a convolutional sub-neural network that performs the actual transformation, and a discriminator that validates target domain authenticity. This segmented architecture manages complexity through modular design while maintaining high adaptation effectiveness
Solution Approach 2:
The patent implements feedback through the discriminator component that provides feedback signals during training to guide the transformation process. The discriminator evaluates whether transformed images match target domain statistics and feeds this information back to adjust the transformation parameters, enabling the complex network to converge to an effective solution through iterative refinement
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the systems includes a domain transformation neural network implemented by one or more computers, wherein the domain transformation neural network is configured to: receive an input image from a source domain; and process a network input comprising the input image from the source domain to generate a transformed image that is a transformation of the input image from the source domain to a target domain that is different from the source domain.