Deep metric learning (DML) involves training a network to learn a
semantically meaningful representation space. Many current approaches mine
n-tuples of examples and model interactions within each tuplets. We present a
novel, compositional DML model, inspired by electrostatic fields in physics
that, instead of in tuples, represents the influence of each example
(embedding) by a continuous potential field, and superposes the fields to
obtain their combined global potential field. We use attractive/repulsive
potential fields to represent interactions among embeddings from images of the
same/different classes. Contrary to typical learning methods, where mutual
influence of samples is proportional to their distance, we enforce reduction in
such influence with distance, leading to a decaying field. We show that such
decay helps improve performance on real world datasets with large intra-class
variations and label noise. Like other proxy-based methods, we also use proxies
to succinctly represent sub-populations of examples. We evaluate our method on
three standard DML benchmarks- Cars-196, CUB-200-2011, and SOP datasets where
it outperforms state-of-the-art baselines.

深度度量学习的一篇研究论文通过使用替代方法，建立了一个组合模型，引入电场物理学的启发，将潜在作用场作用在不同类别图像嵌入之间的交互上，并在实际数据集中实现了优异的性能。