Understanding and Exploiting Dependent Variables with Deep Metric Learning

TL;DR

This paper explores how Deep Metric Learning can be used to understand and leverage variable background features in classification tasks, improving performance by exploiting prior information and clustering in the latent space.

Contribution

It introduces a methodology for visualizing and exploiting background variable influence in DML, including clustering algorithms and online data management strategies.

Findings

Clustering in latent space improves classification accuracy.

Visualization tools reveal variable influence on representations.

Proposed methods enhance DML robustness to feature variability.

Abstract

Deep Metric Learning (DML) approaches learn to represent inputs to a lower-dimensional latent space such that the distance between representations in this space corresponds with a predefined notion of similarity. This paper investigates how the mapping element of DML may be exploited in situations where the salient features in arbitrary classification problems vary over time or due to changing underlying variables. Examples of such variable features include seasonal and time-of-day variations in outdoor scenes in place recognition tasks for autonomous navigation and age/gender variations in human/animal subjects in classification tasks for medical/ethological studies. Through the use of visualisation tools for observing the distribution of DML representations per each query variable for which prior information is available, the influence of each variable on the classification task may be better understood. Based on these relationships, prior information on these salient background variables may be exploited at the inference stage of the DML approach by using a clustering algorithm to improve classification performance. This research proposes such a methodology establishing the saliency of query background variables and formulating clustering algorithms for better separating latent-space representations at run-time. The paper also discusses online management strategies to preserve the quality and diversity of data and the representation of each class in the gallery of embeddings in the DML approach. We also discuss latent works towards understanding the relevance of underlying/multiple variables with DML.