You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: This problem can be more broadly described as image-to-image translation [22], converting an image from one representation of a given scene, x, to another, y, e.g., grayscale to color, image to semantic labels, edge-map to photograph.Years of research in computer vision, image processing, computational photography, and graphics have produced powerful translation systems in the supervised setting, where example image pairs \{x_{i},y_{i}\}_{i=1}^{N} are available (Figure 2, left), e.g.,[11, 19, 22, 23, 28, 33, 45, 56, 58, 62].However, obtaining paired training data can be difficult and expensive. For example, only a couple of datasets exist for tasks like semantic segmentation (e.g., [4]), and they are relatively small.Obtaining input-output pairs for graphics tasks like artistic stylization can be even more difficult since the desired output is highly complex, typically requiring artistic authoring. For many tasks, like object transfiguration (e.g., zebra\leftrightarrowhorse,  Figure 1 top-middle), the desired output is not even well-defined. Figure 8 shows some example results on other paired datasets used in “pix2pix” [22], such as architectural labels\leftrightarrowphotos from the CMP Facade Database [40], and edges\leftrightarrowshoes from the UT Zappos50Kdataset [60]. The image quality of our results is close to those produced by the fully supervised pix2pix while our method learns the mapping without paired supervision.

Question: What is the instance of 'paired examples'?
[Paired training data consists of training examples ({xi, yi}^N i=1), where the correspondence between xi
and yi exists. An instance of 'paired examples' is labels↔photos from the CMP Facade Database.]