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WAVELET-BASED UNSUPERVISED LABEL-TO-IMAGE TRANSLATION

George Eskandar, Mohamed Abdelsamad, Karim Armanious, Shuai Zhang, Bin Yang

  • SPS
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    IEEE Members: $11.00
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    Length: 00:06:50
08 May 2022

Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge amount of paired data to accomplish this task while generic unpaired image-to-image translation frameworks underperform in comparison, because they color-code semantic layouts and learn correspondences in appearance instead of semantic content. Starting from the assumption that a high quality generated image should be segmented back to its semantic layout, we propose a new Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised segmentation loss and whole image wavelet based discrimination. Furthermore, in order to match the high-frequency distribution of real images, a novel generator architecture in the wavelet domain is proposed. We test our methodology on 3 challenging datasets and demonstrate its ability to bridge the performance gap between paired and unpaired models.

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    IEEE Members: Free
    Non-members: Free
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
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    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00