Image Style Transformer Model With Adversarial Feedback Control
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Solution Overview
Problem
Existing image style transfer technologies face challenges in achieving high-quality and efficient style transfer while allowing user control over the style transfer process.
Innovation Solution
The proposed solution involves an apparatus with image processing that uses an image style transformer model to generate a first transfer image, obtain transfer quality evaluation data, calculate a gradient for style transfer loss, and update the first transfer image based on update information generated from the gradient, allowing for user-controlled adjustments through control data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image style transfer is performed using conventional methods, then style transfer can be achieved, but the quality and naturalness of the transferred image deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where a discriminator network evaluates the transferred image and provides feedback to the generator network. The discriminator assesses whether the transferred image maintains natural appearance, and this evaluation is used to adjust the generator's parameters iteratively, improving both image quality and naturalness through continuous refinement.
Solution Approach 2:
The patent employs dynamic parameter adjustment during the style transfer process. The generator and discriminator networks engage in an adversarial dynamic where parameters are continuously updated based on mutual evaluation, allowing the system to adapt and optimize for both quality and naturalness rather than using static transformation rules.
2Manufacturing precision
If image style transfer is performed using conventional methods, then style transfer can be achieved, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the generator and discriminator networks on large datasets before actual style transfer. This pre-training establishes optimized parameter configurations that enable faster convergence during actual style transfer operations, reducing processing time while maintaining high image quality.
Solution Approach 2:
The patent implements continuous optimization through the adversarial training process where the generator and discriminator networks continuously refine their parameters. This continuous useful action allows the system to achieve high-quality transfers more efficiently by maintaining persistent improvement rather than requiring multiple discrete processing stages.
3Ease of operation
If user control over style transfer is added, then user experience improves, but the device complexity increases
Solution Approach 1:
The patent introduces control networks as intermediaries that bridge user input and the complex generator-discriminator system. These control networks accept user-specified parameters (such as style strength, source and target style selection) and translate them into appropriate adjustments for the underlying neural networks, providing user control without exposing the complexity of the adversarial training mechanism.
Data Source
AI summary
An apparatus with image processing includes one or more processors configured to generate a first transfer image corresponding to an input image by performing style transfer on the input image, using an image style transformer model, obtain transfer quality evaluation data on the first transfer image, using the image style transformer model, obtain a gradient for a style transfer loss, based on the transfer quality evaluation data, obtain update information on the first transfer image from an update information generation model to which the gradient is input, and generate a second transfer image by updating the first transfer image, based on the update information.