Synthetic Image Generation for Neural Network Content Detection
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Solution Overview
Problem
Training neural networks to detect non-compliant content in images is challenging due to the lack of available training images and the time-consuming process of manually annotating large collections of images.
Innovation Solution
A system and method that generate synthetic training images by applying random transformations to non-compliant content images and appending them to compliant images, thereby creating a diverse training dataset without the need for manual annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is performed on large collections of training images, then the neural network can be trained to detect non-compliant content, but enormous amounts of time are required and the process becomes impractical
Solution Approach 1:
The patent creates synthetic training images by copying and transforming existing non-compliant images. The system generates multiple variations of non-compliant content through geometric transformations, color changes, and compositional modifications, eliminating the need for manual annotation of large image collections while providing sufficient training data for precise detection
Solution Approach 2:
The patent performs preliminary generation of synthetic training images before the actual training process. By pre-generating diverse non-compliant images with automatic annotations embedded in the synthesis process, the system prepares the training dataset in advance, avoiding time-consuming manual annotation during the training phase
2Adaptability or versatility
If a diverse training dataset is created to improve detection accuracy across various categories, then the neural network performance improves, but the complexity of data collection and annotation increases
Solution Approach 1:
The patent implements a universal image synthesis system that can generate diverse non-compliant images across multiple categories using a single framework. The system applies various transformation operations (geometric, photometric, compositional) that can be uniformly applied to different types of non-compliant content, enabling versatile detection without requiring separate processing pipelines for each category
Solution Approach 2:
The patent systematically varies multiple parameters during synthetic image generation, including geometric transformations (rotation, scaling, translation), photometric changes (brightness, contrast, color), and compositional modifications. These parameter changes create diverse training samples that improve detection versatility across different conditions while maintaining a unified generation process
3Reliability
If appropriate training images are obtained for specific categories of non-compliant content, then the neural network can be trained effectively, but the lack of available training images remains a significant challenge
Solution Approach 1:
The patent overcomes the scarcity of training images by copying existing non-compliant images and generating multiple synthetic variations. Each original non-compliant image serves as a template that is transformed into numerous derivative images through systematic applications of geometric, photometric, and compositional transformations, thereby expanding the training dataset size while maintaining reliability
Solution Approach 2:
The patent extracts key non-compliant content elements from original images and isolates them for targeted transformation and recombination. By extracting the essential non-compliant features and separately manipulating them through various transformations, the system generates diverse training images that preserve the core detection targets while varying contextual characteristics
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable storage media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: training a neural network detection model with a training dataset comprising synthetic training images by: using a transformation algorithm to create the synthetic training images by appending edge case training images to one or more compliant images; receiving, at the neural network detection model, as trained, at least one image; and determining, using the neural network detection model, as trained, whether the at least one image comprises non-compliant content. Other embodiments are disclosed herein.