{"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/robust-place-categorization-with-deep-domain","title":"Robust Place Categorization with Deep Domain Generalization","arxiv_id":"1805.12048","date":"2018-05-30","proceeding":null,"authors":["Massimiliano Mancini","Samuel Rota Bulò","Barbara Caputo","Elisa Ricci"],"abstract":"Traditional place categorization approaches in robot vision assume that\ntraining and test images have similar visual appearance. Therefore, any\nseasonal, illumination and environmental changes typically lead to severe\ndegradation in performance. To cope with this problem, recent works have\nproposed to adopt domain adaptation techniques. While effective, these methods\nassume that some prior information about the scenario where the robot will\noperate is available at training time. Unfortunately, in many cases this\nassumption does not hold, as we often do not know where a robot will be\ndeployed. To overcome this issue, in this paper we present an approach which\naims at learning classification models able to generalize to unseen scenarios.\nSpecifically, we propose a novel deep learning framework for domain\ngeneralization. Our method develops from the intuition that, given a set of\ndifferent classification models associated to known domains (e.g. corresponding\nto multiple environments, robots), the best model for a new sample in the novel\ndomain can be computed directly at test time by optimally combining the known\nmodels. To implement our idea, we exploit recent advances in deep domain\nadaptation and design a Convolutional Neural Network architecture with novel\nlayers performing a weighted version of Batch Normalization. Our experiments,\nconducted on three common datasets for robot place categorization, confirm the\nvalidity of our contribution.","url_abs":"http://arxiv.org/abs/1805.12048v1","url_pdf":"http://arxiv.org/pdf/1805.12048v1.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":"robust-place-categorization-with-deep-domain","repo_url":"https://github.com/mancinimassimiliano/caffe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.12048","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}