How do the neural networks distinguish two images? It is of critical importance to understand
the matching mechanism of deep models for developing reliable intelligent systems for …

Deep metric learning papers from the past four years have consistently claimed great
advances in accuracy, often more than doubling the performance of decade-old methods. In …

Learning the distance metric between pairs of examples is of great importance for learning
and visual recognition. With the remarkable success from the state of the art convolutional …

Deep metric learning, in which the loss function plays a key role, has proven to be extremely
useful in visual recognition tasks. However, existing deep metric learning loss functions such …

Metric learning seeks to embed images of objects such that class-defined relations are
captured by the embedding space. However, variability in images is not just due to different …

The core of deep metric learning (DML) involves learning visual similarities in high-
dimensional embedding space. One of the main challenges is to generalize from seen …

Metric learning involves learning a discriminative representation such that embeddings of
similar classes are encouraged to be close, while embeddings of dissimilar classes are …

Deep metric learning has been extensively explored recently, which trains a deep neural
network to produce discriminative embedding features. Most existing methods usually …

Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at
learning a mapping from the input domain to an embedding space, where semantically …

This work explores the visual explanation for deep metric learning and its applications. As
an important problem for learning representation, metric learning has attracted much …