Deep Metric Learning (DML) methods have been proven relevant for visual
similarity learning. However, they sometimes lack generalization properties
because they are trained often using an inappropriate sample selection strategy
or due to the difficulty of the dataset caused by a distributional shift in the
data. These represent a significant drawback when attempting to learn the
underlying data manifold. Therefore, there is a pressing need to develop better
ways of obtaining generalization and representation of the underlying manifold.
In this paper, we propose a novel approach to DML that we call Guided Deep
Metric Learning, a novel architecture oriented to learning more compact
clusters, improving generalization under distributional shifts in DML. This
novel architecture consists of two independent models: A multi-branch master
model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced
hypothesis space based on prior knowledge from labeled data, which guides or
regularizes the decision boundary of a student model during training under an
offline knowledge distillation scheme. Experiments have shown that the proposed
method is capable of a better manifold generalization and representation to up
to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave
et al. to perform a more fair and realistic comparison, which is currently
absent in the literature

本文提出了一种新的由两个独立模型构成的度量学习结构 Guided Deep Metric Learning，通过使用 Few-Shot Learning 的角度来生成一个基于带标签数据的先验知识的减少的假设空间，之后通过离线的知识蒸馏方案来指导或规则化学生模型的决策边界，提高在分布偏移下的一般化，并获得了高达 40% 的 (Recall@1, CIFAR10) 改进。