{"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/interpolation-between-residual-and-non","title":"Interpolation between Residual and Non-Residual Networks","arxiv_id":"2006.05749","date":"2020-06-10","proceeding":null,"authors":["Zonghan Yang","Yang Liu","Chenglong Bao","Zuoqiang Shi"],"abstract":"Although ordinary differential equations (ODEs) provide insights for designing network architectures, its relationship with the non-residual convolutional neural networks (CNNs) is still unclear. In this paper, we present a novel ODE model by adding a damping term. 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