{"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/implementing-adaptive-separable-convolution","title":"Implementing Adaptive Separable Convolution for Video Frame Interpolation","arxiv_id":"1809.07759","date":"2018-09-20","proceeding":null,"authors":["Mart Kartašev","Carlo Rapisarda","Dominik Fay"],"abstract":"As Deep Neural Networks are becoming more popular, much of the attention is\nbeing devoted to Computer Vision problems that used to be solved with more\ntraditional approaches. Video frame interpolation is one of such challenges\nthat has seen new research involving various techniques in deep learning. In\nthis paper, we replicate the work of Niklaus et al. on Adaptive Separable\nConvolution, which claims high quality results on the video frame interpolation\ntask. We apply the same network structure trained on a smaller dataset and\nexperiment with various different loss functions, in order to determine the\noptimal approach in data-scarce scenarios. The best resulting model is still\nable to provide visually pleasing videos, although achieving lower evaluation\nscores.","url_abs":"http://arxiv.org/abs/1809.07759v1","url_pdf":"http://arxiv.org/pdf/1809.07759v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"implementing-adaptive-separable-convolution","repo_url":"https://github.com/martkartasev/sepconv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"implementing-adaptive-separable-convolution","repo_url":"https://github.com/ekgibbons/pytorch-sepconv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"implementing-adaptive-separable-convolution","repo_url":"https://github.com/carlo-/sepconv-ios","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"video-frame-interpolation","task_name":"Video Frame Interpolation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}