Neural Network Image Transformation via Semantic Coefficient Adjustment
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Solution Overview
Problem
Current methods for generating images, such as those using generative adversarial networks (GANs), face challenges in effectively transforming images by altering specific semantic elements like gender, age, or facial expressions while maintaining the overall image quality and realism.
Innovation Solution
A method involving a neural network that extracts coefficients corresponding to semantic elements from an input image, allows for the selection and modification of target coefficients, and applies these changes to basis vectors in an embedding space to generate transformed images, utilizing an encoder to map the image to this space and a generator to produce the transformed image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GAN-based image generation methods are used to transform images by altering semantic elements, then image quality and realism are improved, but controllability and precision in transforming specific semantic elements deteriorate
Solution Approach 1:
The patent segments the image representation into discrete semantic elements (gender, age, expression, etc.) by identifying specific anchor points and their corresponding basis vectors in the embedding space. This allows independent control over each semantic element while maintaining overall image quality through the GAN framework.
Solution Approach 2:
The patent changes parameters by modifying coefficients of selected basis vectors corresponding to target semantic elements. By adjusting these coefficients within the embedding space and regenerating the image, precise control over specific semantic transformations is achieved while preserving image realism through the trained GAN model.
2Ease of operation
If traditional image transformation methods are used to alter semantic elements, then ease of operation is improved, but manufacturing precision and transformation accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary embedding space with basis vectors that mediates between the input image and the transformed output. This embedding space acts as a controlled intermediate representation where semantic transformations can be precisely manipulated through coefficient adjustments before regenerating the final image, thereby achieving both ease of operation and high transformation accuracy.
Data Source
AI summary
A method with generation of a transformed image includes: receiving an input image; extracting, from the input image, coefficients corresponding to semantic elements of the input image; selecting at least one first target coefficient, among the coefficients, corresponding to at least one target semantic element that is to be changed among the semantic elements of the input image; changing the at least one first target coefficient; and generating a transformed image from the input image by applying the coefficients, including the changed at least one first target coefficient, to basis vectors used to represent the semantic elements of the input image in an embedding space of a neural network, the basis vectors corresponding to the semantic elements of the input image.