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README.md

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 # SinGAN
-### ICCV 2019 Best paper award (Marr prize)
 
 [Project](https://tamarott.github.io/SinGAN.htm) | [Arxiv](https://arxiv.org/pdf/1905.01164.pdf) | [CVF](http://openaccess.thecvf.com/content_ICCV_2019/papers/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf) | [Supplementary materials](https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Shaham_SinGAN_Learning_a_ICCV_2019_supplemental.pdf) | [Talk (ICCV`19)](https://youtu.be/mdAcPe74tZI?t=3191) 
-### Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image" 
+### Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"
+#### ICCV 2019 Best paper award (Marr prize)
+
 
 ## Random samples from a *single* image
 With SinGAN, you can train a generative model from a single natural image, and then generate random samples from the given image, for example: