USIS: Unsupervised Semantic Image Synthesis

• URL: http://arxiv.org/abs/2109.14715v1
• Date: Wed, 29 Sep 2021 20:48:41 GMT
• Title: USIS: Unsupervised Semantic Image Synthesis
• Authors: George Eskandar, Mohamed Abdelsamad, Karim Armanious, Bin Yang
• Abstract summary: We propose a new Unsupervised paradigm for Semantic Image Synthesis (USIS) USIS learns to output images with visually separable semantic classes using a self-supervised segmentation loss. In order to match the color and texture distribution of real images without losing high-frequency information, we propose to use whole image wavelet-based discrimination.
• Score: 9.613134538472801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a photorealistic image is synthesized from a segmentation mask. SIS has mostly been addressed as a supervised problem. However, state-of-the-art methods depend on a huge amount of labeled data and cannot be applied in an unpaired setting. On the other hand, generic unpaired image-to-image translation frameworks underperform in comparison, because they color-code semantic layouts and feed them to traditional convolutional networks, which then learn correspondences in appearance instead of semantic content. In this initial work, we propose a new Unsupervised paradigm for Semantic Image Synthesis (USIS) as a first step towards closing the performance gap between paired and unpaired settings. Notably, the framework deploys a SPADE generator that learns to output images with visually separable semantic classes using a self-supervised segmentation loss. Furthermore, in order to match the color and texture distribution of real images without losing high-frequency information, we propose to use whole image wavelet-based discrimination. We test our methodology on 3 challenging datasets and demonstrate its ability to generate multimodal photorealistic images with an improved quality in the unpaired setting.

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