Backward Compatible Embedding Model for Image Search
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Solution Overview
Problem
Facial recognition systems face challenges when updating embedding models, as previous embeddings become useless, requiring users to reprocess and reupload images, leading to increased computational costs and maintenance burdens.
Innovation Solution
The implementation of backward compatible embedding models allows new models to generate embeddings that are interchangeable with previous versions, eliminating the need for reprocessing existing data through a training method that enforces compatibility with previous classifier models, enabling backfill-free updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a new embedding model is introduced to improve recognition accuracy, then recognition accuracy is improved, but existing embeddings become incompatible requiring reprocessing of all images
Solution Approach 1:
The patent introduces a compatibility layer that acts as an intermediary between the new embedding model and existing embeddings. This layer translates or adapts the new model's outputs to be compatible with the existing embedding space, allowing the new model to improve accuracy without requiring reprocessing of all existing images. The compatibility layer mediates between the incompatible representations.
Solution Approach 2:
The patent modifies certain parameters of the embedding model deployment, such as allowing multiple versioned embedding models to coexist or transforming the output parameters of the new model to match the existing embedding schema. This enables parameter adjustment that maintains compatibility while incorporating improvements from the new model.
2Measurement precision
If a new embedding model is introduced to improve recognition accuracy, then recognition accuracy is improved, but computational costs increase due to reprocessing requirements
Solution Approach 1:
The compatibility layer serves as a computational intermediary that avoids the need to reprocess all existing images through the new embedding model. Instead, it efficiently transforms or adapts existing embeddings, significantly reducing the computational energy required compared to full reprocessing.
Solution Approach 2:
The system performs preliminary actions by maintaining compatibility structures in advance that allow new models to be integrated without triggering costly reprocessing operations. The compatibility layer is prepared beforehand to handle translations between model versions efficiently.
3Adaptability or versatility
If embedding models are updated frequently to improve performance, then system adaptability is improved, but maintenance burden increases due to backfill requirements
Solution Approach 1:
The compatibility layer acts as a maintenance intermediary that simplifies the process of updating embedding models. It automatically handles the translation and adaptation tasks when models are updated, reducing the manual maintenance burden and complexity associated with frequent model updates.
Solution Approach 2:
The compatibility layer provides universal functionality that works across multiple embedding model versions. This multi-functional component enables the system to maintain adaptability to different model versions while presenting a unified interface that simplifies maintenance operations.
4Ease of operation
If backward compatibility is enforced to eliminate reprocessing, then ease of operation is improved, but the new model may not fully utilize its optimized features
Solution Approach 1:
The compatibility layer is designed as an intelligent intermediary that preserves the full capabilities of the new model while ensuring backward compatibility. It performs selective transformations that maintain performance-critical features while only adapting the necessary aspects for compatibility with existing embeddings.
Data Source
AI summary
Techniques for building a backward compatible and backfill-free image search system are described. According to some embodiments, a backwards compatible training system trains a new embedding model to be backward compatible with the face embeddings (e.g., floating-point vectors) generated by a previous embedding model. In one embodiment, backwards compatible training uses a classifier of the previous embedding model as a form of constraint in the training of the new embedding model.