Guided Design Generation Using GAN Layer Feedback
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Solution Overview
Problem
Generative adversarial networks (GANs) lack the ability to understand user-specified requirements and provide feedback on vaguely defined features, limiting their ability to generate designs that meet specific user-defined criteria.
Innovation Solution
A method that guides GANs to generate designs by allowing users to interact with the models through positive and negative image samples, identifying feature layers, determining feature expression variations, and refining designs based on user feedback, enabling the inclusion of multiple features in a single latent vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional GANs are used for design generation, then new output data can be generated with the same statistics as the training set, but the system cannot understand user-specified requirements or incorporate vaguely defined features
Solution Approach 1:
The patent implements a feedback mechanism where users can provide positive and negative image samples to guide the GAN generation process. The system processes user feedback through layer identification and feature expression variation analysis, continuously refining generated designs to better match user requirements. This closed-loop feedback system enables the GAN to adapt to user preferences while maintaining the statistical properties of training data.
Solution Approach 2:
The patent introduces an intermediary processing layer between user feedback and the GAN model. This intermediary system identifies specific feature layers in the GAN, determines feature expression variations, and translates vague user requirements into actionable guidance for the generation process. This mediator enables effective communication between human users and the machine learning model.
2Manufacturing precision
If users provide positive and negative image samples to guide feature inclusion, then designs can be refined based on user feedback, but the process requires identifying specific layers and determining feature expression variations
Solution Approach 1:
The system implements self-service through automated layer identification and feature expression analysis. When users provide positive and negative image samples, the system automatically identifies which layers generate the desired features and determines the appropriate expression variations, eliminating the need for users to manually specify technical parameters. This automation maintains high design precision while reducing operational complexity.
Solution Approach 2:
The patent performs preliminary analysis of the GAN architecture to identify feature layers and their expressions before the actual design generation begins. By pre-processing the model structure and understanding feature representations in advance, the system streamlines the user feedback process and enables more precise control over design outcomes without increasing user burden.
3Adaptability or versatility
If multiple features are embedded in a single latent vector, then designs can incorporate multiple user-defined features, but it becomes difficult to control individual feature expression levels
Solution Approach 1:
The patent segments the control of multiple features by identifying which specific layers in the GAN are responsible for generating each feature. Even though multiple features are embedded in a single latent vector, the system can selectively manipulate individual layers to control the expression level of specific features independently. This layer-level segmentation enables precise control over multi-feature designs.
Solution Approach 2:
The system applies local quality control by determining feature expression variations for specific layers rather than uniformly adjusting the entire latent vector. Each layer can be individually tuned to achieve the desired expression level for its associated feature, allowing different parts of the design to have different levels of feature expression while maintaining overall coherence.
Data Source
AI summary
A method for guided design generation includes receiving a plurality of image samples for an intended design and generating a first plurality of images by providing a positive latent vector to a first layer out of a plurality of layers and a negative latent vector to remaining layers out of the plurality of layers. Responsive to receiving a first image selection, identifying the first layer out of the plurality of layers generated the first image includes a feature of interest. Determining a feature expression variation for the feature of interest in latent space based on the ranking of a first plurality of generated sample images, wherein the first plurality of generated sample images includes the feature of interest. Responsive to receiving a second plurality of images for a plurality of base features, generating an initial design based on the feature of interest and the plurality of base features.