{"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/image-inpainting-for-irregular-holes-using","title":"Image Inpainting for Irregular Holes Using Partial Convolutions","arxiv_id":"1804.07723","date":"2018-04-20","proceeding":"ECCV 2018 9","authors":["Guilin Liu","Fitsum A. Reda","Kevin J. Shih","Ting-Chun Wang","Andrew Tao","Bryan Catanzaro"],"abstract":"Existing deep learning based image inpainting methods use a standard\nconvolutional network over the corrupted image, using convolutional filter\nresponses conditioned on both valid pixels as well as the substitute values in\nthe masked holes (typically the mean value). This often leads to artifacts such\nas color discrepancy and blurriness. Post-processing is usually used to reduce\nsuch artifacts, but are expensive and may fail. We propose the use of partial\nconvolutions, where the convolution is masked and renormalized to be\nconditioned on only valid pixels. We further include a mechanism to\nautomatically generate an updated mask for the next layer as part of the\nforward pass. Our model outperforms other methods for irregular masks. 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