GAN Generator Learning With Segmented Latent Feature Control
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Solution Overview
Problem
Existing generative adversarial networks (GANs) struggle to accurately associate specific portions of a latent code with individual features, limiting the accuracy of controlling multiple features in generated images.
Innovation Solution
A learning system that divides the latent code into portion codes corresponding to individual features, using mapping networks and image synthesis networks to generate images, and employs a trained discriminator for learning, enhancing the generator's ability to recognize feature associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the generator uses the entire latent code to generate images, then the generation process is simple, but the accuracy of associating specific features with latent code portions is poor
Solution Approach 1:
The patent divides the latent code into multiple portion codes, where each portion code corresponds to specific features. The generator includes multiple mapping networks that separately process different latent code portions and multiple image synthesis networks that synthesize image components based on these portion codes. This segmentation enables precise association between latent code portions and features while maintaining structured complexity in the network architecture.
2Manufacturing precision
If the generator processes the entire latent code at once, then the processing is efficient, but the control over individual features is inaccurate
Solution Approach 1:
The latent code is segmented into multiple portion codes, each handled by dedicated mapping networks. This allows parallel processing of different feature components, improving both precision and efficiency. Each mapping network processes specific latent code portions independently, enabling precise feature control while reducing overall processing time through parallelization.
Solution Approach 2:
Instead of processing the entire latent code uniformly, the system applies partial action by processing only specific latent code portions through dedicated mapping networks. This selective processing approach improves feature control precision while optimizing time efficiency by avoiding redundant processing of all latent code components.
3Measurement precision
If mapping networks are introduced to process latent code portions, then feature association accuracy improves, but the network complexity increases
Solution Approach 1:
The system introduces multiple mapping networks that segment the latent code processing task. Each mapping network is responsible for specific latent code portions and corresponds to particular features. This segmentation improves mapping accuracy while organizing network complexity in a structured, manageable manner rather than creating monolithic complexity.
Solution Approach 2:
The mapping networks are designed with multi-functionality, where each mapping network can process specific latent code portions and generate corresponding feature representations. This universal design allows the same architectural pattern to be reused across different feature mappings, reducing overall system complexity despite the presence of multiple networks.
Data Source
AI summary
A learning system for executing learning of a generator of a generative adversarial network (GAN) which allows a user to control a plurality of features relating to a generated image, the learning system comprising at least one processor configured to: acquire a plurality of portion codes respectively corresponding to the plurality of features based on a latent code for generating the generated image and a plurality of mapping networks respectively corresponding to the plurality of features; generate the generated image based on image synthesis networks configured to generate the generated image through use of the plurality of portion codes; and execute the learning of the generator including the plurality of mapping networks and the image synthesis networks based on the generated image and a trained discriminator of the GAN.