Proxy-Based Distance Metric Learning for Faster Convergence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing distance metric learning methods face challenges in optimizing triplet-based losses due to sampling issues, requiring the selection of informative pairs or triplets, which slows down convergence rates.
Innovation Solution
The approach introduces a proxy-based method where a machine-learned distance model is trained using a loss function that compares an anchor data point's embedding to positive and negative proxies, eliminating the need for explicit triplet selection and allowing faster convergence by approximating the training dataset with a smaller proxy space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If triplet-based loss functions are used for distance metric learning, then the model can learn meaningful distance metrics, but the convergence rate is slow due to sampling issues in selecting informative pairs or triplets
Solution Approach 1:
The patent introduces proxies as intermediary representations that stand in for actual data points in the loss computation. Instead of directly comparing anchor points with positive and negative samples, the method uses proxy embeddings as mediators. This intermediary approach simplifies the sampling problem by replacing the need to select informative triplets from the entire dataset with comparing against a small set of learned proxy representations, thereby accelerating convergence while maintaining learning quality.
Solution Approach 2:
The patent creates proxy embeddings as simplified copies or representations of the actual data distribution. These proxies are learned during training and serve as surrogate targets that capture the essential characteristics of positive and negative samples without requiring explicit triplet selection. The copying principle allows the model to learn from these representative proxies rather than exhaustively sampling from the full dataset, improving computational efficiency and convergence speed.
2Measurement precision
If explicit triplet selection is performed to optimize loss functions, then learning accuracy can be improved, but computational complexity and time consumption increase
Solution Approach 1:
The patent extracts the essential learning signal from the complex triplet selection process by identifying and isolating the key comparative relationships. Instead of requiring explicit selection of informative triplets, the method extracts the core learning objective into a simplified loss function that operates on proxy representations. This extraction removes the computational burden of triplet mining while preserving the essential accuracy-improving comparisons between anchor, positive, and negative samples.
Solution Approach 2:
The patent performs preliminary learning by training proxy representations before using them in the main loss computation. These proxies are pre-learned to capture the data distribution characteristics, allowing the subsequent training phase to focus on refining the distance metric without the overhead of real-time triplet selection. This preliminary action separates the complex sampling task from the metric learning task, reducing overall computational complexity.
Data Source
AI summary
The present disclosure provides systems and methods that enable distance metric learning using proxies. A machine-learned distance model can be trained in a proxy space in which a loss function compares an embedding provided for an anchor data point of a training dataset to a positive proxy and one or more negative proxies, where each of the positive proxy and the one or more negative proxies serve as a proxy for two or more data points included in the training dataset. Thus, each proxy can approximate a number of data points, enabling faster convergence. According to another aspect, the proxies of the proxy space can themselves be learned parameters, such that the proxies and the model are trained jointly. Thus, the present disclosure enables faster convergence (e.g., reduced training time). The present disclosure provides example experiments which demonstrate a new state of the art on several popular training datasets.