Directional GAN for Attribute-Controlled Image Generation
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Solution Overview
Problem
Conventional methods for generating tailored images for target audiences are inefficient and fail to consistently produce high-quality, high-resolution images with desired attributes, relying on manual creation or unreliable generative adversarial neural networks (GANs) that require multiple attempts and are computationally inefficient.
Innovation Solution
An attribute-based image generation system using a directional-GAN architecture, comprising an image generation neural network, an image-attribute classifier, and a latent-attribute classifier, allows direct control over latent vectors to generate high-resolution images with specific attributes by shifting latent vectors within the latent space to align with desired attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional GANs are used for image generation, then image creation can be automated, but the process is computationally inefficient and requires multiple attempts to achieve desired attributes
Solution Approach 1:
The patent applies preliminary action by pre-training the GAN model with attribute information during the training phase. The attribute classifier is trained to recognize specific attributes (e.g., clothing style, pose) in images, and this knowledge is integrated into the generator. During inference, instead of requiring multiple random attempts, the system可以直接指定期望的属性来生成图像，因为生成器已经学习了属性与图像特征之间的映射关系。
Solution Approach 2:
The patent implements feedback through the attribute classifier that provides attribute predictions for generated images. The classifier feedback loop allows the system to verify whether generated images possess the desired attributes, and this feedback mechanism guides the generation process to ensure attribute compliance without requiring multiple random attempts.
2Manufacturing precision
If manual image creation is used to ensure desired attributes, then image quality and attribute accuracy are high, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of image creation with an automated neural network-based system. The generator network automatically creates images with desired attributes by processing attribute inputs through learned transformations, eliminating the need for manual image editing while maintaining attribute accuracy through the trained attribute classifier.
Solution Approach 2:
The patent utilizes parameter changes by transforming attribute parameters (e.g., clothing style labels, pose descriptors) directly into image generation parameters. The system changes the state of the generation process from random sampling to controlled parameter-based generation, where specific attribute parameters guide the creation of images with precise attribute control.
3Extent of automation
If conventional GANs are used for image generation, then automation is achieved, but reliability in producing images with specific attributes is low
Solution Approach 1:
The attribute classifier provides continuous feedback during the generation process to ensure attribute consistency. The classifier monitors generated images and provides feedback signals that guide the generator to maintain desired attributes, creating a closed-loop system that reliably produces images with specific attributes.
Solution Approach 2:
The system performs preliminary training to establish reliable attribute-image mappings before actual generation. During training, the model learns the relationship between attribute parameters and visual features, preprocessing the knowledge needed for reliable attribute-controlled generation. This preliminary learning phase ensures that subsequent generation operations consistently produce images with the desired attributes.
Data Source
AI summary
Embodiments of the present disclosure are directed towards generating images conditioned on a desired attribute. In particular, an attribute-based image generation system can use a directional-GAN architecture to generate images conditioned on a desired attribute. A latent vector and a desired attribute are received. A feature subspace is determined for the latent vector using a latent-attribute linear classifier trained to determine a relationship between the latent vector and the desired attribute. An image is generated using the latent vector such that the image contains the desired attribute. In embodiments, where the feature space differs from a desired feature subspace, a directional vector is applied to the latent vector that shifts the latent vector from the feature subspace to the desired feature subspace. This modified latent vector is then used during generation of the image.