Image Generation Network Training for Semantic Similarity

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing image generation neural networks struggle to generate high-quality images that are semantically similar to a specific sub-distribution of images, requiring computationally expensive fine-tuning and large datasets that may not be available.

Innovation Solution

Train an image generation neural network using a training dataset of image pairs identified as semantically similar from the same web page, leveraging a pre-trained conditioning image encoder neural network to capture semantic attributes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If an image generation neural network is trained on a large broad distribution dataset, then it can generate images representing the broad distribution well, but it cannot generate images representing specific sub-distributions (semantic contexts) effectively

Engineering Contradiction:

Improvebroad distribution coverageVSAvoidsemantic similarity for sub-distribution

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The patent segments the training process into two distinct stages: pre-training on a broad distribution of images to learn general visual patterns, and then fine-tuning on a smaller subset of images that represent specific semantic contexts. This segmentation allows the model to first acquire general capabilities and then specialize in generating semantically similar images for target distributions without requiring separate models for each distribution.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies preliminary action by performing pre-training on a large broad distribution dataset before fine-tuning on specific sub-distributions. This preliminary training establishes a robust foundation of visual understanding and generation capabilities that enables the model to subsequently adapt to specific semantic contexts more effectively, avoiding the need to train from scratch for each distribution.

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If the image generation neural network is fine-tuned on a smaller dataset representing a specific sub-distribution, then it can generate images semantically similar to the sub-distribution, but the computational cost and risk of over-fitting increase

Engineering Contradiction:

Improvesemantic similarity for sub-distributionVSAvoidcomputational cost of fine-tuning

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by performing fine-tuning on only a subset of the training data that represents the target semantic context, rather than using the entire broad distribution dataset. This partial fine-tuning approach allows the model to specialize in generating semantically similar images for specific distributions while significantly reducing the computational resources required compared to training on all available data.

Inventive Principle:

Principle #16Partial or excessive action

3Manufacturing precision

If separate fine-tuning is performed for every specific sub-distribution, then each sub-distribution can be optimized, but the overall process becomes computationally expensive and impractical

Engineering Contradiction:

Improveoptimization for each sub-distributionVSAvoidefficiency of training process

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent implements universality by training a single image generation neural network on a broad distribution that encompasses multiple semantic contexts, then using a unified fine-tuning approach that can adapt the same model to different sub-distributions. This universal model eliminates the need to train and maintain separate specialized models for each semantic context, significantly improving computational efficiency and productivity while still achieving optimized generation for each target distribution.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250363783A1Training image generation neural networks to generate semantically similar images

Publication Date: 2025.11.27 GDM HOLDING LLC

AI summary

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training an image generation neural network and, once the image generation neural network is trained, generating new output images using the image generation neural network. In particular, the described techniques include obtaining a training data set that includes training examples that each include a training conditioning image and training target image that has been identified to being semantically similar to the training conditioning image. Then training, on the training data set, an image generation neural network that is configured to generate an output image conditioned on a conditioning image. By using the described techniques to train an image generation neural network the system achieves high quality image generation that can be used to generate new output images semantically similar to a conditioning image without the need to fine-tune the image generation neural network to a specific subset of semantic attributes.