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We show results on image style transfer, where a\nfeed-forward network is trained to solve the optimization problem proposed by\nGatys et al in real-time. Compared to the optimization-based method, our\nnetwork gives similar qualitative results but is three orders of magnitude\nfaster. We also experiment with single-image super-resolution, where replacing\na per-pixel loss with a perceptual loss gives visually pleasing results.","short_abstract":"We consider image transformation problems, where an input image is transformed into an output image.","url_abs":"http://arxiv.org/abs/1603.08155v1","url_pdf":"http://arxiv.org/pdf/1603.08155v1.pdf","authors":"[\"Justin Johnson\", \"Alexandre Alahi\", \"Li Fei-Fei\"]","published":"2016-03-27T00:00:00Z","tasks":"[\"Image Super-Resolution\", \"Nuclear Segmentation\", \"Speech Enhancement\", \"Style Transfer\", \"Super-Resolution\"]","methods":"[]","has_code":false,"code_links":[{"ID":245,"CreatedAt":"2026-02-27T13:01:31Z","UpdatedAt":"2026-02-27T13:01:31Z","DeletedAt":null,"paper_id":35157,"paper_url":"https://paperswithcode.com/paper/perceptual-losses-for-real-time-style","paper_title":"Perceptual Losses for Real-Time Style Transfer and 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