{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/perceptual-losses-for-real-time-style","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","arxiv_id":"1603.08155","date":"2016-03-27","proceeding":null,"authors":["Justin Johnson","Alexandre Alahi","Li Fei-Fei"],"abstract":"We consider image transformation problems, where an input image is\ntransformed into an output image. Recent methods for such problems typically\ntrain feed-forward convolutional neural networks using a \\emph{per-pixel} loss\nbetween the output and ground-truth images. Parallel work has shown that\nhigh-quality images can be generated by defining and optimizing\n\\emph{perceptual} loss functions based on high-level features extracted from\npretrained networks. We combine the benefits of both approaches, and propose\nthe use of perceptual loss functions for training feed-forward networks for\nimage transformation tasks. 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.","url_abs":"http://arxiv.org/abs/1603.08155v1","url_pdf":"http://arxiv.org/pdf/1603.08155v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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