SinGAN: Learning a Generative Model from a Single Natural Image

ICCV 2019 Tamar Rott ShahamTali DekelTomer Michaeli

We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image... (read more)

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