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. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.

SinGAN是一种无条件生成模型，可以从单个自然图像中进行学习，并且可以用于多种图像操作任务。其包含一系列基于全卷积神经网络的生成器，可以学习图像的不同尺度上的内部数据分布，并生成具有相同视觉内容的高质量、多样化的样本。用户研究表明，生成的样本往往被误认为是真实图像。

SinGAN：从单张自然图像学习生成模型