Style Embedding Extraction via Weakly Supervised Neural Networks
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Solution Overview
Problem
Conventional digital image search systems are inflexible and inaccurate in identifying fine-grain digital image styles due to their reliance on explicit labeling and limited annotated datasets, which restricts their ability to adapt to diverse styles and leads to coarse, high-level style determination.
Innovation Solution
The use of a weakly supervised neural network architecture that learns style embeddings from large-scale digital content groupings without explicit labeling, combining autoencoders and discriminative neural networks to generate style embeddings for query images and identify similar styles in a repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use explicit labeling and limited annotated datasets, then the system structure is simple and easy to implement, but the style determination accuracy is coarse and limited to high-level styles
Solution Approach 1:
The patent replaces the mechanical labeling system with a neural network-based automatic style extraction system. The style extraction neural network automatically learns style representations from digital images without requiring explicit human labeling, thereby improving measurement precision (style determination accuracy) while the automation reduces long-term operational complexity despite initial system complexity increases
Solution Approach 2:
The patent changes the fundamental parameter of style representation from discrete labeled classes to continuous style embeddings. This transformation allows the system to capture fine-grained style variations by representing styles as continuous vectors in a high-dimensional space, enabling more precise style determination beyond coarse categorical labels
2Adaptability or versatility
If conventional systems rely on labeled ontology of digital image styles, then the system is easy to operate with fixed style classes, but the adaptability to identify new styles is limited
Solution Approach 1:
The patent transforms the static labeled ontology into a dynamic style embedding space. The neural network continuously learns and adapts style representations from new data without requiring manual ontology updates, enabling the system to automatically identify and adapt to new styles while maintaining ease of operation through automatic adaptation
Solution Approach 2:
The style extraction neural network serves multiple functions: it extracts styles from training images, generates style embeddings for query images, and automatically adapts to new styles without requiring separate processing pipelines. This multi-functionality improves adaptability while maintaining operational simplicity through a unified system
3Reliability
If conventional systems use human annotation for style labeling, then the implementation process is simple, but the style classification accuracy is affected by subjective labeling
Solution Approach 1:
The patent implements self-service by enabling the neural network to automatically extract and label styles from digital images without human intervention. The style extraction neural network serves itself by learning style representations directly from image data, eliminating subjective human annotation while improving reliability through consistent automated classification
Solution Approach 2:
The patent substitutes the mechanical human annotation process with an automated neural network-based style extraction system. This replacement eliminates subjectivity in labeling while maintaining implementation feasibility through automatic processing, thereby improving reliability without requiring complex human annotation workflows
4Measurement precision
If conventional systems are limited to fixed labeled classes, then the system complexity is low, but the ability to distinguish fine variations in style is insufficient
Solution Approach 1:
The patent transitions from zero-dimensional discrete style labels to high-dimensional continuous style embeddings. This dimensional transformation enables fine-grain style discrimination by representing styles as points in a continuous vector space, where subtle style variations can be distinguished through small distance differences, overcoming the limitations of fixed categorical labels
Solution Approach 2:
The patent changes the style representation parameter from discrete class labels to continuous embedding vectors. This parameter transformation enables the system to capture and distinguish fine-grained style variations by representing styles as continuous values in a high-dimensional space, allowing for more precise style discrimination despite increased computational complexity
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly identifying digital images with similar style to a query digital image using fine-grain style determination via weakly supervised style extraction neural networks. For example, the disclosed systems can extract a style embedding from a query digital image using a style extraction neural network such as a novel two-branch autoencoder architecture or a weakly supervised discriminative neural network. The disclosed systems can generate a combined style embedding by combining complementary style embeddings from different style extraction neural networks. Moreover, the disclosed systems can search a repository of digital images to identify digital images with similar style to the query digital image. The disclosed systems can also learn parameters for one or more style extraction neural network through weakly supervised training without a specifically labeled style ontology for sample digital images.