  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 attention recently, while the interpretation of
such model is not as well studied as classification. To this end, we propose an
intuitive idea to show where contributes the most to the overall similarity of
two input images by decomposing the final activation. Instead of only providing
the overall activation map of each image, we propose to generate point-to-point
activation intensity between two images so that the relationship between
different regions is uncovered. We show that the proposed framework can be
directly deployed to a large range of metric learning applications and provides
valuable information for understanding the model. Furthermore, our experiments
show its effectiveness on two potential applications, i.e. cross-view pattern
discovery and interactive retrieval. The source code is available at
\url{https://github.com/Jeff-Zilence/Explain_Metric_Learning}.
