Image-to-Image Translation Using Shared Latent Space
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Solution Overview
Problem
Traditional neural network training for image-to-image translation requires correlated image pairs, making it difficult and resource-intensive to collect paired examples, especially when translating between domains like daytime and nighttime scenes.
Innovation Solution
The method involves encoding images from different domains into a shared latent space, allowing for unsupervised training without the need for correlated pairs, using a system with three neural networks: two for encoding and one for generating translated images based on shared latent codes, with weight values computed from these codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised training with correlated image pairs is used, then translation accuracy is improved, but data collection difficulty and time increase
Solution Approach 1:
The patent introduces a shared latent space as an intermediary representation that connects the source and target domains without requiring direct paired examples. The variational autoencoder learns to map both domains to this shared latent space, enabling translation through the mediator rather than direct supervised pairing.
Solution Approach 2:
The method uses unpaired images from both domains to learn the translation function. Instead of requiring exact correlated pairs, the system learns from the statistical properties and distributions of unpaired images, effectively copying the essential characteristics needed for translation without the overhead of precise pairing.
2Measurement precision
If supervised training with correlated image pairs is used, then translation accuracy is improved, but resource requirements increase
Solution Approach 1:
The shared latent space serves as a mediator that allows the model to learn from unpaired images. By mapping both source and target domain images to this intermediate representation, the system can learn translation without requiring correlated pairs, thus reducing data resource requirements.
Solution Approach 2:
The patent changes the training paradigm from supervised (requiring paired data) to unsupervised (using unpaired data). This parameter change in the training approach allows the model to learn effective translations with fewer and less stringent data requirements.
3Productivity
If unpaired images are used for training, then data collection efficiency is improved, but training complexity increases
Solution Approach 1:
The patent segments the translation task into multiple components: an encoder for the source domain, an encoder for the target domain, and a shared latent space representation. This segmentation allows each component to be optimized independently while working together to achieve the overall translation goal.
Solution Approach 2:
The shared latent space serves multiple functions: it represents both source and target domain images, enables translation between domains, and provides a common framework for learning from unpaired data. This multi-functionality reduces the need for separate specialized models.
Data Source
AI summary
A method, computer readable medium, and system are disclosed for training a neural network. The method includes the steps of encoding, by a first neural network, a first image represented in a first domain to convert the first image to a shared latent space, producing a first latent code and encoding, by a second neural network, a second image represented in a second domain to convert the second image to a shared latent space, producing a second latent code. The method also includes the step of generating, by a third neural network, a first translated image in the second domain based on the first latent code, wherein the first translated image is correlated with the first image and weight values of the third neural network are computed based on the first latent code and the second latent code.