A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity. Previous attempts learned such an embedding in a supervised manner, requiring the availability of many annotated images. In this paper, we present an unsupervised embedding of natural image patches, avoiding the need for annotated images. The key idea is that the similarity of two patches can be learned from the prevalence of their spatial proximity in natural images. Clearly, relying on this simple principle, many spatially nearby pairs are outliers. However, as we show, these outliers do not harm the convergence of the metric learning. We show that our unsupervised embedding approach is more effective than a supervised one or one that uses deep patch representations. Moreover, we show that it naturally lends itself to an efficient self-supervised domain adaptation technique onto a target domain that contains a common foreground object.

中文翻译：

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无监督的自然图像补丁学习

天然图像补丁的度量标准是分析图像的重要工具。一种有效的学习方法是训练一个深度网络，将图像补丁映射到向量空间，其中欧几里得距离反映补丁的相似性。先前的尝试以有监督的方式学习了这种嵌入，这需要许多带注释的图像。在本文中，我们提出了自然图像补丁的无监督嵌入，从而避免了对带注释图像的需求。关键思想是，可以从自然图像中其空间接近度的普遍程度来了解两个斑块的相似性。显然，依靠这一简单原理，许多空间上相邻的对是离群值。但是，正如我们所展示的，这些离群值不会损害度量学习的收敛性。我们表明，我们的无监督嵌入方法比使用深补丁表示的一种或多种监督方法更为有效。此外，我们证明了它自然地将一种有效的自我监督域自适应技术引入到包含公共前景对象的目标域中。