Proxy-Based Distance Metric Learning With Learnable Margins
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Solution Overview
Problem
Existing distance metric learning methods, particularly proxy-based approaches, face challenges in selecting optimal margin values, which is often time-consuming and requires expertise, and do not effectively address data-to-data relationships.
Innovation Solution
A method and system that treat the margin as a learnable parameter, using intra-class and inter-class losses with adaptive margin parameters to adjust the model's confidence levels, reducing the need for manual tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If proxy-based metric learning is used to reduce training complexity, then the number of input pairs is significantly reduced, but the method does not exploit data-to-data relationships
Solution Approach 1:
The patent introduces 'proxies' as intermediary objects that represent groups of same-class data points. These proxies serve as mediators between the raw data and the learning process, allowing the model to capture data-to-data relationships indirectly through proxy-data interactions while maintaining reduced training complexity
Solution Approach 2:
The patent segments the training process into two distinct loss components: proxy-data loss for capturing data-to-data relationships and data-data loss for capturing data-to-proxy relationships. This segmentation allows both relationship types to be learned simultaneously without increasing overall training complexity
2Reliability
If manual hyperparameter selection is performed to optimize margin values, then performance may be improved, but the process is time-consuming and requires expertise
Solution Approach 1:
The patent implements self-service hyperparameter optimization by introducing learnable margin parameters that are automatically adjusted during training through backpropagation. The model learns optimal margin values for different classes without requiring manual intervention, grid search, or expertise in hyperparameter tuning
Solution Approach 2:
The patent changes the nature of margin parameters from fixed hyperparameters requiring manual selection to learnable parameters that are automatically optimized during training. This parameter transformation allows the system to adapt margins dynamically based on data characteristics and training progress
3Loss of information
If pair-based metric learning is used to capture data relationships, then data-to-data relationships are considered, but training complexity is high and convergence is slow
Solution Approach 1:
The patent uses proxies as intermediaries to represent groups of same-class data points, reducing the number of pairs that need to be processed during training. This intermediary approach maintains the ability to capture data relationships while significantly improving training efficiency and convergence speed
Data Source
AI summary
A method and a system for performing distance metric learning using proxies are provided. The method for performing distance metric learning assigns a proxy as an anchor to represent a class and associates the proxy with all data points in a training batch. The method allows data points to interact with each other via proxies during training. Additionally, the fine-grained data-to-data relation is actively considered, which is combined with a learnable margin parameter leading to intra-class compactness and inter-class separability.