  Deep Metric Learning (DML) serves to learn an embedding function to project
semantically similar data into nearby embedding space and plays a vital role in
many applications, such as image retrieval and face recognition. However, the
performance of DML methods often highly depends on sampling methods to choose
effective data from the embedding space in the training. In practice, the
embeddings in the embedding space are obtained by some deep models, where the
embedding space is often with barren area due to the absence of training
points, resulting in so called "missing embedding" issue. This issue may impair
the sample quality, which leads to degenerated DML performance. In this work,
we investigate how to alleviate the "missing embedding" issue to improve the
sampling quality and achieve effective DML. To this end, we propose a
Densely-Anchored Sampling (DAS) scheme that considers the embedding with
corresponding data point as "anchor" and exploits the anchor's nearby embedding
space to densely produce embeddings without data points. Specifically, we
propose to exploit the embedding space around single anchor with Discriminative
Feature Scaling (DFS) and multiple anchors with Memorized Transformation
Shifting (MTS). In this way, by combing the embeddings with and without data
points, we are able to provide more embeddings to facilitate the sampling
process thus boosting the performance of DML. Our method is effortlessly
integrated into existing DML frameworks and improves them without bells and
whistles. Extensive experiments on three benchmark datasets demonstrate the
superiority of our method.
