Feature Quantization Model Training Using Domain Decomposition
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Solution Overview
Problem
Feature quantization algorithms face challenges in guaranteeing performance when labeling information is scarce, particularly in specific target data domains, leading to inadequate feature quantization codes and increased computational complexity.
Innovation Solution
A method for training a feature quantization model that decomposes feature information from multiple source data domains to extract common and domain-specific features, using abundant labeling information from relevant source domains to create a common feature quantization model that can improve performance in data domains with inadequate labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If feature quantization is performed using traditional algorithms, then computational speed is improved, but performance accuracy deteriorates when labeling information is scarce
Solution Approach 1:
The patent applies preliminary action by pre-training a common feature quantization model using abundant labeling information from multiple source data domains before deploying it to target domains with scarce labels. This pre-training phase prepares the model in advance to handle label-scarce scenarios, allowing fast inference without requiring extensive label-specific training when deployed.
Solution Approach 2:
The patent creates a universal common feature quantization model that can be applied across multiple different data domains (image, text, audio, etc.) despite their differences. The model learns domain-agnostic feature representations that generalize well to various target domains, making it multi-functional rather than domain-specific.
2Reliability
If domain-specific feature quantization models are trained separately for each target domain, then performance accuracy is improved, but storage and computational complexity increase
Solution Approach 1:
The patent segments feature information into two distinct components: common features that are shared across all domains and domain-specific features that are unique to each target domain. This segmentation allows the system to use a single common model for most tasks while only training lightweight domain-specific adapters when needed, reducing overall complexity.
Solution Approach 2:
The patent merges the common feature quantization model with domain-specific feature quantization models by combining their outputs. The common model provides general feature representations while domain-specific models add specialized knowledge, and their combined output achieves high performance without requiring separate full-scale models for each domain.
3Speed
If feature information is processed without decomposition, then processing speed is maintained, but the ability to leverage common patterns across domains is lost
Solution Approach 1:
The patent extracts common feature patterns from multiple source data domains by processing feature information through the common feature quantization model. This extraction process identifies and isolates domain-agnostic representations that can be reused across different target domains, preventing loss of valuable shared information.
Data Source
AI summary
A method for training a feature quantization model includes: obtaining a plurality of source data domains; obtaining feature information and labeling information of each of the plurality of source data domains; decomposing the feature information of each of the plurality of source data domains, so as to obtain common feature information and domain-specific feature information of the plurality of source data domains, the common feature information being feature information common to the plurality of source data domains; and training a feature quantization model according to the common feature information of all the source data domains, and domain-specific feature information and the labeling information of each of the plurality of source data domains, so as to obtain a common feature quantization model.