SinGAN: Learning a Generative Model from a Single Natural Image

Tamar Rott Shaham, Tali Dekel, Tomer Michaeli, ICCV-2019


This paper proposes a novel GAN training technique to obtain a generative model that can be learned using a single image. Unlike some of the previous works that used single image for training for a single task, the model can be used for unconditional generative modelling, not limited to texture images. This paper also got the best paper award in ICCV 2019

Main Contributions

Implementation Details

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