General Feature Vectors for Reconstruction and Fingerprint Discrimination
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Solution Overview
Problem
Conventional techniques for generating representational forms of media data are inefficient and inflexible, lacking versatility in updating historical archives and creating task-specific representations, leading to redundancies and inefficiencies as new applications and technologies emerge.
Innovation Solution
A unified representation learning system that jointly trains machine-learning-based models for feature extraction, signal processing, reconstruction, and fingerprint generation, enabling the generation of general feature vectors that support multiple tasks, allowing for efficient updating and maintenance of archives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional techniques are used for generating representational forms of media data, then the process is simple and straightforward, but the system lacks versatility and flexibility in updating historical archives and creating task-specific representations
Solution Approach 1:
The patent implements a unified representation learning system that performs multiple functions: feature extraction, signal conditioning, signal recovery, and reconstruction, all within a single integrated architecture. This allows the system to handle diverse tasks including updating historical archives and creating task-specific representations, thereby achieving versatility without requiring separate systems for each function.
Solution Approach 2:
The system employs dynamic training iterations where the unified model is repeatedly trained on historical archives with different task-specific configurations. This dynamic approach allows the system to adapt to new tasks and update archives flexibly, achieving adaptability while maintaining a single unified structure rather than creating static separate systems.
2Productivity
If separate models are trained for each task (image reconstruction, fingerprint generation, etc.), then each model can be optimized for its specific task, but redundancies and inefficiencies arise
Solution Approach 1:
The patent merges previously separate models (feature extraction, signal conditioning, signal recovery, reconstruction) into a single unified representation learning model. This consolidation eliminates redundancies in processing pipelines and reduces the overall computational overhead required for maintaining archives, thereby improving productivity while reducing waste.
Solution Approach 2:
The unified model serves multiple purposes: it extracts features, conditions signals, recovers signals, and reconstructs images all within a single framework. This multi-functionality eliminates the need for separate optimized models for each task, reducing redundancies while maintaining efficiency across all operations including archive updates.
3Adaptability or versatility
If a unified representation learning system is implemented, then versatility and flexibility are improved, but the device complexity increases
Solution Approach 1:
While unified in structure, the system segments the training process into distinct functional components (feature extraction, signal conditioning, signal recovery, reconstruction) that can be trained and optimized separately before being integrated. This segmentation approach manages complexity by allowing focused development of each component while maintaining the benefits of a unified system.
Solution Approach 2:
The patent introduces general feature vectors as intermediary representations that bridge different tasks and functions within the unified system. These intermediate representations simplify the complexity by providing a common interface between various operations, making the unified system more manageable while preserving its versatility.
4Productivity
If conventional separate models are used, then implementation is straightforward, but real-time and historical analysis of media content becomes inefficient
Solution Approach 1:
The unified representation learning system combines multiple processing functions into a single integrated model, eliminating the sequential overhead of running separate models for feature extraction, signal conditioning, recovery, and reconstruction. This merging reduces the total processing time required for both real-time and historical media content analysis, thereby improving productivity and reducing time loss.
Data Source
AI summary
Methods and systems are disclosed for generating general feature vectors (GFVs), each simultaneously constructed for separate tasks of image reconstruction and fingerprint-based image discrimination. The computing system may include machine-learning-based components configured for extracting GFVs from images, signal processing for both transmission and reception and recovery of the extracted GFVs, generating reconstructed images from the recovered GFVs, and discriminating between fingerprints generated from the recovered GFVs and query fingerprints generated from query GFVs. A set of training images may be received at the computing system. In each of one or more training iterations over the set of training images, the components may be jointly trained with each training image of the set by minimizing a joint loss function computed as a sum of losses due to signal processing and recovery, image reconstruction, and fingerprint discrimination. The trained components may be configured for runtime implementation among one or more computing devices.