Multimodal Image Generation via Shared Neural Network Layers
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Solution Overview
Problem
Existing methods for generating multimodal digital images rely on one-to-one correspondences between different modalities, which are challenging to determine, limiting the ability to generate images without such correspondences in the training data.
Innovation Solution
Jointly training neural networks with a weight sharing constraint, where the structure and number of layers are identical, but with some layers having identical and others having different parameters, to produce common and modality-specific features respectively, allowing the generation of multimodal digital images without relying on one-to-one correspondences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If one-to-one correspondence methods are used to generate multimodal digital images, then the generated images maintain consistent semantic meaning across modalities, but the method becomes inapplicable when one-to-one correspondences do not exist in the training data
Solution Approach 1:
The patent uses a generator network to copy and transform semantic information from one modality to another. The generator learns to map input images from one modality to corresponding images in another modality by copying underlying semantic features while transforming appearance characteristics, enabling generation without requiring explicit one-to-one correspondence in training data
Solution Approach 2:
The patent introduces a semantic embedding space as an intermediary representation between different modalities. Instead of directly mapping between modalities, the system transforms images into a shared semantic space where correspondence is established through semantic similarity rather than pixel-level alignment, allowing generation when traditional correspondences are absent
2Ease of manufacture
If physical models are used to generate corresponding images in different modalities, then the generation process can leverage domain knowledge, but the method is limited to specific applications such as image super-resolution or deblurring
Solution Approach 1:
The patent transforms the problem from applying modality-specific physical models to learning modality-agnostic parameter transformations. The generator network learns to transform parameters in a shared semantic space rather than applying fixed physical models, enabling generalization across diverse modalities including but not limited to super-resolution and deblurring applications
Solution Approach 2:
The patent creates a universal framework that can handle multiple different modality transformations through a single generator network. The system is designed to be multi-functional, capable of performing various image-to-image translation tasks across different modalities without requiring separate specialized models for each application
3Manufacturing precision
If neural networks are trained independently to generate digital images, then each network can be optimized for its specific modality, but the generated images are not related across modalities
Solution Approach 1:
The patent merges multiple modality-specific neural networks into a unified generator framework with shared layers. The network architecture combines modality-specific processing capabilities with shared semantic understanding, ensuring that generated images across different modalities maintain consistent semantic relationships while preserving modality-specific characteristics
Data Source
AI summary
A computer-implemented method generates a multimodal digital image by processing a vector with a first neural network to produce a first modality of the digital image and processing the vector with a second neural network to produce a second modality of the digital image. A structure and a number of layers of the first neural network are identical to a structure and a number of layers of the second neural network. Also, at least one layer in the first neural network has parameters identical to parameters of a corresponding layer in the second neural network, and at least one layer in the first neural network has parameters different from parameters of a corresponding layer in the second neural network.