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Learning a metric of natural image patches is an important tool for analyzingimages. An efficient means is to train a deep network to map an image patch toa vector space, in which the Euclidean distance reflects patch similarity.Previous attempts learned such an embedding in a supervised manner, requiringthe availability of many annotated images. In this paper, we present anunsupervised embedding of natural image patches, avoiding the need forannotated images. The key idea is that the similarity of two patches can belearned from the prevalence of their spatial proximity in natural images.Clearly, relying on this simple principle, many spatially nearby pairs areoutliers, however, as we show, the outliers do not harm the convergence of themetric learning. We show that our unsupervised embedding approach is moreeffective than a supervised one or one that uses deep patch representations.Moreover, we show that it naturally leads itself to an efficientself-supervised domain adaptation technique onto a target domain that containsa common foreground object.

Unsupervised Natural Image Patch Learning