GAN Abstract Background Generation via Multimodal Embeddings
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Solution Overview
Problem
Generative Adversarial Networks (GANs) suffer from mode collapse, resulting in repetitive and unrealistic generated images, especially when dealing with abstract patterns, due to their reliance on class-conditional information that fails to capture the full range of variation in training data.
Innovation Solution
Incorporating a multimodal encoder that generates rich and diverse image embeddings, allowing the GAN to condition on these embeddings instead of class labels, enabling the generation of diverse abstract images by combining them with noise vectors through a mapping network to produce latent vectors for image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GANs use class-conditional information for image generation, then the generation process is controllable and editable, but the generated images become repetitive and suffer from mode collapse
Solution Approach 1:
The patent changes the conditioning parameters from discrete class labels to continuous image embeddings. The mapping network transforms the embedding space to generate diverse latent vectors that maintain editability while escaping mode collapse, allowing controlled generation of varied abstract images
Solution Approach 2:
The patent introduces a mapping network as an intermediary between the image embeddings and the generator. This mapping network transforms the embedding representations into latent vectors that condition the generator, enabling diverse image generation while preserving control through the embedding space
2Device complexity
If GANs rely on class labels for conditioning, then the model structure remains simple, but the model fails to capture the full range of variation in training data
Solution Approach 1:
The patent transitions from one-dimensional class labels to high-dimensional image embeddings. This dimensional expansion allows the model to capture nuanced variations in the training data while maintaining a relatively simple overall architecture through the use of pre-trained embedding models
Data Source
AI summary
Systems and methods for generating abstract backgrounds are described. Embodiments are configured to obtain an input prompt, encode the input prompt to obtain a prompt embedding, and generate a latent vector based on the prompt embedding and a noise vector. Embodiments include a multimodal encoder configured to generate the prompt embedding, which is an intermediate representation the prompt. In some cases, the prompt includes or indicates an “abstract background” type image. The latent vector is generated using a mapping network of a generative adversarial network (GAN). Embodiments are further configured to generate an image based on the latent vector using the GAN.