  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.
