Single Neural Network for Multi-Domain Image Generation
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Solution Overview
Problem
Existing methods for generating modified digital images using machine learning models, such as GANs, face challenges in efficiently adapting to new data domains without forgetting previously learned knowledge, and require storing multiple neural networks for each domain, which is costly in terms of storage and generalization.
Innovation Solution
The proposed solution involves training a single neural network that can generate images with multiple semantic parameters by fine-tuning a generative model to preserve original knowledge and represent new knowledge along predetermined linear directions in the latent space, allowing for minimal retraining for different modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple neural networks are stored for each data domain, then the model can adapt to different domains, but storage cost increases and generalization ability deteriorates
Solution Approach 1:
The patent merges multiple domain-specific neural networks into a single unified neural network that can handle multiple data domains. This is achieved by training the single network on data from multiple domains simultaneously, allowing it to learn domain-specific features while sharing common representations, thereby reducing storage requirements while maintaining adaptability.
Solution Approach 2:
The patent creates a universal neural network that serves multiple functions across different data domains. The single network is designed to be multi-functional, capable of processing and generating images from various domains (e.g., different artistic styles, different object categories) without requiring separate specialized networks for each domain.
2Adaptability or versatility
If multiple neural networks are stored for each data domain, then the model can adapt to different domains, but device complexity increases
Solution Approach 1:
The patent combines multiple domain-specific processing systems into a single integrated neural network architecture. This merging eliminates the need for complex system management of multiple separate networks, simplifying the overall system structure while maintaining the capability to handle multiple domains through unified processing.
3Quantity of substance
If a single neural network is used for multiple domains, then storage cost is reduced, but the model may forget previously learned knowledge
Solution Approach 1:
The patent applies preliminary action by pre-training the single neural network on data from multiple domains before deployment. This pre-training establishes a foundation of knowledge across domains, and the network is then fine-tuned on specific domains as needed. This preliminary exposure to multiple domains helps prevent catastrophic forgetting when the network adapts to new domains.
Solution Approach 2:
The patent implements feedback mechanisms during training and inference to monitor and preserve previously learned knowledge. By incorporating regularization techniques and evaluation metrics that track performance across all domains, the system receives feedback that guides the network to maintain proficiency in previously learned domains while adapting to new ones.
Data Source
AI summary
Systems and methods for image generation include obtaining an input image and an attribute value representing an attribute of the input image to be modified; computing a modified latent vector for the input image by applying the attribute value to a basis vector corresponding to the attribute in a latent space of an image generation network; and generating a modified image based on the modified latent vector using the image generation network, wherein the modified image includes the attribute based on the attribute value.