{"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/partial-convolution-based-padding","title":"Partial Convolution based Padding","arxiv_id":"1811.11718","date":"2018-11-28","proceeding":null,"authors":["Guilin Liu","Kevin J. Shih","Ting-Chun Wang","Fitsum A. Reda","Karan Sapra","Zhiding Yu","Andrew Tao","Bryan Catanzaro"],"abstract":"In this paper, we present a simple yet effective padding scheme that can be\nused as a drop-in module for existing convolutional neural networks. We call it\npartial convolution based padding, with the intuition that the padded region\ncan be treated as holes and the original input as non-holes. Specifically,\nduring the convolution operation, the convolution results are re-weighted near\nimage borders based on the ratios between the padded area and the convolution\nsliding window area. Extensive experiments with various deep network models on\nImageNet classification and semantic segmentation demonstrate that the proposed\npadding scheme consistently outperforms standard zero padding with better\naccuracy.","url_abs":"http://arxiv.org/abs/1811.11718v1","url_pdf":"http://arxiv.org/pdf/1811.11718v1.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":"partial-convolution-based-padding","repo_url":"https://github.com/NVIDIA/partialconv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"partial-convolution-based-padding","repo_url":"https://github.com/feixuetuba/inpainting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"partial-convolution-based-padding","repo_url":"https://github.com/feixuetuba/inpating","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"partial-convolution-based-padding","repo_url":"https://github.com/lessw2020/auto-adaptive-ai","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.11718","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}