Training End-to-end Single Image Generators without GANs

• URL: http://arxiv.org/abs/2004.06014v1
• Date: Tue, 7 Apr 2020 17:58:03 GMT
• Title: Training End-to-end Single Image Generators without GANs
• Authors: Yael Vinker and Nir Zabari and Yedid Hoshen
• Abstract summary: AugurOne is a novel approach for training single image generative models. Our approach trains an upscaling neural network using non-affine augmentations of the (single) input image. A compact latent space is jointly learned allowing for controlled image synthesis.
• Score: 27.393821783237186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present AugurOne, a novel approach for training single image generative models. Our approach trains an upscaling neural network using non-affine augmentations of the (single) input image, particularly including non-rigid thin plate spline image warps. The extensive augmentations significantly increase the in-sample distribution for the upsampling network enabling the upscaling of highly variable inputs. A compact latent space is jointly learned allowing for controlled image synthesis. Differently from Single Image GAN, our approach does not require GAN training and takes place in an end-to-end fashion allowing fast and stable training. We experimentally evaluate our method and show that it obtains compelling novel animations of single-image, as well as, state-of-the-art performance on conditional generation tasks e.g. paint-to-image and edges-to-image.

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