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- W4377010153 abstract "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." @default.
- W4377010153 created "2023-05-19" @default.
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- W4377010153 date "2023-05-16" @default.
- W4377010153 modified "2023-09-29" @default.
- W4377010153 title "Wavelet-based Unsupervised Label-to-Image Translation" @default.
- W4377010153 doi "https://doi.org/10.48550/arxiv.2305.09647" @default.
- W4377010153 hasPublicationYear "2023" @default.
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