{"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/revisiting-batch-normalization-for-practical","title":"Revisiting Batch Normalization For Practical Domain Adaptation","arxiv_id":"1603.04779","date":"2016-03-15","proceeding":null,"authors":["Yanghao Li","Naiyan Wang","Jianping Shi","Jiaying Liu","Xiaodi Hou"],"abstract":"Deep neural networks (DNN) have shown unprecedented success in various\ncomputer vision applications such as image classification and object detection.\nHowever, it is still a common annoyance during the training phase, that one has\nto prepare at least thousands of labeled images to fine-tune a network to a\nspecific domain. Recent study (Tommasi et al. 2015) shows that a DNN has strong\ndependency towards the training dataset, and the learned features cannot be\neasily transferred to a different but relevant task without fine-tuning. In\nthis paper, we propose a simple yet powerful remedy, called Adaptive Batch\nNormalization (AdaBN) to increase the generalization ability of a DNN. By\nmodulating the statistics in all Batch Normalization layers across the network,\nour approach achieves deep adaptation effect for domain adaptation tasks. In\ncontrary to other deep learning domain adaptation methods, our method does not\nrequire additional components, and is parameter-free. It archives\nstate-of-the-art performance despite its surprising simplicity. Furthermore, we\ndemonstrate that our method is complementary with other existing methods.\nCombining AdaBN with existing domain adaptation treatments may further improve\nmodel performance.","url_abs":"http://arxiv.org/abs/1603.04779v4","url_pdf":"http://arxiv.org/pdf/1603.04779v4.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":"revisiting-batch-normalization-for-practical","repo_url":"https://github.com/erlendd/ddan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.04779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.04779"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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