Unsupervised Image-to-Image Translation via Style-Content Separation
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Solution Overview
Problem
Existing image-to-image translation methods struggle to effectively separate and extract content and style features from images, leading to inconsistent results, as they often rely on two independent encoders and require paired training data, which limits their applicability and quality.
Innovation Solution
A unified framework that uses a single encoder to extract feature information and a style-content separation module to measure correlation with high-level visual tasks, allowing for the generation of target images without paired data, and incorporates a normalized feature fusion method to reduce the 'water drop phenomenon and improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two independent encoders are used to separately extract content and style features, then feature separation is attempted, but the content features cannot be effectively focused on meaningful objects and style features cannot extract different styles of different objects
Solution Approach 1:
The patent segments the feature extraction process by using a single encoder to extract comprehensive features, then employs a style-content separation module that measures correlation with high-level visual tasks to divide features into content and style components. This segmentation approach resolves the contradiction by achieving both separation accuracy and content focus reliability through correlation-based feature allocation.
Solution Approach 2:
The patent introduces a style-content separation module as an intermediary between the encoder and the translation process. This module measures the correlation of feature information with high-level visual tasks and uses this measurement to effectively separate content features (those highly correlated with visual tasks) from style features (those less correlated), thereby resolving the contradiction between separation accuracy and content focus reliability.
2Measurement precision
If additional constraints from high-level visual tasks are introduced, then content feature learning is improved, but different network architectures need to be designed for specific tasks reducing extendibility
Solution Approach 1:
The patent implements a universal style-content separation module that can be applied across different high-level visual tasks without requiring task-specific network architecture redesign. The module measures correlation between feature information and visual tasks, then separates content and style features based on this measurement. This universal approach maintains content feature learning accuracy while significantly improving method extendibility to multiple tasks.
Solution Approach 2:
The patent changes the approach from designing different network architectures for different tasks to using a single flexible separation mechanism that adapts to different tasks by measuring feature correlation with task requirements. This parameter-based adaptation (measuring correlation degrees) allows the same network architecture to be effectively applied to various high-level visual tasks, resolving the contradiction between learning accuracy and extendibility.
3Ease of manufacture
If unsupervised translation is implemented without paired data, then data requirements are reduced, but feature separation becomes more difficult without ground truth guidance
Solution Approach 1:
The patent implements a self-service feature separation mechanism that does not require paired training data or ground truth guidance. The style-content separation module measures the correlation of feature information with high-level visual tasks and automatically separates content and style features based on this self-measured correlation. This self-service approach maintains ease of data preparation while achieving effective feature separation precision through intrinsic feature-property relationships.
Data Source
AI summary
The embodiments of this disclosure disclose an unsupervised image-to-image translation method. A specific implementation of this method comprises: obtaining an initial image, and zooming the initial image to a specific size; performing spatial feature extraction on the initial image to obtain feature information; inputting the feature information to a style-content separation module to obtain content feature information and style feature information; generating reference style feature information of a reference image in response to obtaining the reference image, and setting the reference style feature information as a Gaussian noise in response to not obtaining the reference image; inputting the content feature information and the reference style feature information into a generator to obtain a target image; and zooming the target image to obtain a final target image. This implementation can be applied to a variety of different high-level visual tasks, and improve the expandability of the whole system.