{"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/decorrelated-batch-normalization","title":"Decorrelated Batch Normalization","arxiv_id":"1804.08450","date":"2018-04-23","proceeding":"CVPR 2018 6","authors":["Lei Huang","Dawei Yang","Bo Lang","Jia Deng"],"abstract":"Batch Normalization (BN) is capable of accelerating the training of deep\nmodels by centering and scaling activations within mini-batches. In this work,\nwe propose Decorrelated Batch Normalization (DBN), which not just centers and\nscales activations but whitens them. We explore multiple whitening techniques,\nand find that PCA whitening causes a problem we call stochastic axis swapping,\nwhich is detrimental to learning. We show that ZCA whitening does not suffer\nfrom this problem, permitting successful learning. DBN retains the desirable\nqualities of BN and further improves BN's optimization efficiency and\ngeneralization ability. We design comprehensive experiments to show that DBN\ncan improve the performance of BN on multilayer perceptrons and convolutional\nneural networks. Furthermore, we consistently improve the accuracy of residual\nnetworks on CIFAR-10, CIFAR-100, and ImageNet.","url_abs":"http://arxiv.org/abs/1804.08450v1","url_pdf":"http://arxiv.org/pdf/1804.08450v1.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":"decorrelated-batch-normalization","repo_url":"https://github.com/umich-vl/DecorrelatedBN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"decorrelated-batch-normalization","repo_url":"https://github.com/bhneo/DecorrelatedBN_tf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"decorrelated-batch-normalization","repo_url":"https://github.com/bhneo/decorrelated_bn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"decorrelated-batch-normalization","repo_url":"https://github.com/huangleiBuaa/DecorrelatedBN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"decorrelated-batch-normalization","repo_url":"https://github.com/huangleiBuaa/IterNorm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"decorrelated-batch-normalization","repo_url":"https://github.com/huangleiBuaa/IterNorm-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"decorrelated-batch-normalization","method_name":"Decorrelated Batch Normalization"},{"method_slug":"pca","method_name":"PCA"},{"method_slug":"pca-whitening","method_name":"PCA Whitening"},{"method_slug":"zca-whitening","method_name":"ZCA Whitening"}],"datasets_introduced":[],"methods_introduced":[{"slug":"decorrelated-batch-normalization","name":"Decorrelated Batch Normalization","full_name":"Decorrelated Batch Normalization"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.08450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.08450"}},"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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