Neural Network Style Transfer Without Paired Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image transformation technologies struggle to adapt images from a source domain to a target domain while preserving semantic content, often requiring paired images or being inefficient in generating target domain images that resemble the real-world environment, which can be costly or difficult to obtain.
Innovation Solution
A neural network system that transforms images from a source domain to a target domain by using specific low-level and shared high-level encoder and decoder layers, allowing for style transfer without paired images, and trains the networks jointly to maintain semantic consistency and adapt pixel value distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing image transformation technologies are used to adapt images from source domain to target domain, then the transformation can be performed, but paired images are required which are costly or difficult to obtain
Solution Approach 1:
The patent introduces a domain adaptation layer as an intermediary component between the source domain encoder and target domain decoder. This layer learns to transform features from source domain distribution to target domain distribution without requiring paired images, thereby resolving the contradiction between ease of data acquisition and transformation quality
Solution Approach 2:
The transformation system is segmented into distinct functional modules: source domain encoder, domain adaptation layer, and target domain decoder. This segmentation allows each component to be optimized independently, with the domain adaptation layer specifically addressing the distribution mismatch without needing paired training data
2Ease of manufacture
If traditional style transfer methods are used, then style transformation can be achieved, but semantic content is altered in the process
Solution Approach 1:
The patent performs domain adaptation at the feature level before final image reconstruction. By adapting the feature distribution early in the pipeline while preserving semantic information, the method prevents semantic loss that would occur if style transfer were applied directly to pixel values
Solution Approach 2:
The domain adaptation layer serves as an intermediary that transforms feature distributions without directly manipulating pixel values. This intermediate feature-space transformation preserves semantic content while achieving the desired target domain appearance
3Measurement precision
If paired images are used for training, then transformation accuracy is improved, but data acquisition cost and difficulty increase significantly
Solution Approach 1:
The system uses unpaired images from both source and target domains to train the domain adaptation layer. The method is self-sufficient in that it does not require external paired data, automatically learning the domain transformation from independently collected unpaired datasets
Solution Approach 2:
The patent changes the training paradigm from requiring paired images to accepting unpaired images. By modifying the loss function and training approach to work with unpaired data while maintaining transformation accuracy, the method eliminates the need for costly paired data acquisition
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for semantically-consistent image style transfer. One of the methods includes: receiving an input source domain image; processing the source domain image using one or more source domain low-level encoder neural network layers to generate a low-level representation; processing the low-level representation using one more high-level encoder neural network layers to generate an embedding of the input source domain image; processing the embedding using one or more high-level decoder neural network layers to generate a high-level feature representation of features of the input source domain image; and processing the high-level feature representation of the features of the input source domain image using one or more target domain low-level decoder neural network layers to generate an output target domain image that is from the target domain but that has similar semantics to the input source domain image.