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 the metric learning 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 applied to a wide range of metric learning applications and provides valuable information for model understanding. Both theoretical and empirical analyses are provided to demonstrate the superiority of the proposed overall activation map over existing methods. Furthermore, our experiments validate the effectiveness of the proposed point-specific activation map on two applications, i.e. cross-view pattern discovery and interactive retrieval. Code is available at https://github.com/Jeff-Zilence/Explain_Metric_Learning

中文翻译：

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深度度量学习的视觉解释


这项工作探索了深度度量学习的视觉解释及其应用。作为学习表示的一个重要问题，度量学习最近引起了广泛的关注，但度量学习模型的解释却不像分类那样得到充分研究。为此，我们提出了一个直观的想法，通过分解最终的激活来显示哪里对两个输入图像的整体相似性贡献最大。我们建议生成两个图像之间的点对点激活强度，而不是仅提供每个图像的整体激活图，以便揭示不同区域之间的关系。我们表明，所提出的框架可以直接应用于广泛的度量学习应用，并为模型理解提供有价值的信息。理论和实证分析都证明了所提出的整体激活图相对于现有方法的优越性。此外，我们的实验验证了所提出的特定点激活图在两个应用程序（即跨视图模式发现和交互式检索）上的有效性。代码可在 https://github.com/Jeff-Zilence/Explain_Metric_Learning 获取