{"url":"/method/switchable-normalization","slug":"switchable-normalization","name":"Switchable Normalization","full_name":"Switchable Normalization","full_name_withheld":false,"description_markdown":"**Switchable Normalization** combines three types of statistics estimated channel-wise, layer-wise, and minibatch-wise by using [instance normalization](https://paperswithcode.com/method/instance-normalization), [layer normalization](https://paperswithcode.com/method/layer-normalization), and [batch normalization](https://paperswithcode.com/method/batch-normalization) respectively. [Switchable Normalization](https://paperswithcode.com/method/switchable-normalization) switches among them by learning their importance weights.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"http://arxiv.org/abs/1806.10779v5","title":"Differentiable Learning-to-Normalize via Switchable Normalization","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/switchablenorms/Switchable-Normalization/blob/5472286952ba5519a7d3229d9ad899a5c1b2e5b1/devkit/ops/switchable_norm.py#L60","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Normalization","url":"/methods/category/normalization","pwc_aliases":[]}],"n_papers_tagged":9,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/block-attention-and-switchable-normalization","title":"Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels","date":"2023-04-10","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/reducing-the-feature-divergence-of-rgb-and","title":"Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization","date":"2021-06-06","arxiv_id":"2106.03088","n_code_links":1,"syntology":null},{"paper":null,"title":"Stable and Effective One-Step Method for Person Search","date":"2021-05-13","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks","date":"2020-07-07","arxiv_id":"2007.03294","n_code_links":0,"syntology":null},{"paper":null,"title":"Exemplar Normalization for Learning Deep Representation","date":"2020-03-19","arxiv_id":"2003.08761","n_code_links":0,"syntology":null},{"paper":"/paper/adapted-center-and-scale-prediction-more","title":"Adapted Center and Scale Prediction: More Stable and More Accurate","date":"2020-02-20","arxiv_id":"2002.09053","n_code_links":1,"syntology":null},{"paper":null,"title":"Switchable Normalization for Learning-to-Normalize Deep Representation","date":"2019-07-22","arxiv_id":"1907.10473","n_code_links":0,"syntology":null},{"paper":null,"title":"Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?","date":"2018-11-19","arxiv_id":"1811.07727","n_code_links":0,"syntology":null},{"paper":"/paper/differentiable-learning-to-normalize-via","title":"Differentiable Learning-to-Normalize via Switchable Normalization","date":"2018-06-28","arxiv_id":"1806.10779","n_code_links":3,"syntology":{"ran":0,"of":13,"unverified":13,"pointer_only":0}}],"papers_shown":9,"tasks":[{"task":null,"name":"GPU","papers":2},{"task":"/task/pedestrian-detection","name":"Pedestrian Detection","papers":2},{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/ischemic-stroke-lesion-segmentation","name":"Ischemic Stroke Lesion Segmentation","papers":1},{"task":"/task/lesion-segmentation","name":"Lesion Segmentation","papers":1},{"task":"/task/multi-task-learning","name":"Multi-Task Learning","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/person-re-identification","name":"Person Re-Identification","papers":1},{"task":"/task/person-search","name":"Person Search","papers":1},{"task":"/task/region-proposal","name":"Region Proposal","papers":1},{"task":"/task/retinal-vessel-segmentation","name":"Retinal Vessel Segmentation","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":14,"n_tasks":14,"usage_by_year":[{"year":"2018","papers":2},{"year":"2019","papers":1},{"year":"2020","papers":3},{"year":"2021","papers":2},{"year":"2023","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/switchable-normalization"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}