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However, DBN relies\nheavily on either a large batch size, or eigen-decomposition that suffers from\npoor efficiency on GPUs. We propose Iterative Normalization (IterNorm), which\nemploys Newton's iterations for much more efficient whitening, while\nsimultaneously avoiding the eigen-decomposition. Furthermore, we develop a\ncomprehensive study to show IterNorm has better trade-off between optimization\nand generalization, with theoretical and experimental support. To this end, we\nexclusively introduce Stochastic Normalization Disturbance (SND), which\nmeasures the inherent stochastic uncertainty of samples when applied to\nnormalization operations. With the support of SND, we provide natural\nexplanations to several phenomena from the perspective of optimization, e.g.,\nwhy group-wise whitening of DBN generally outperforms full-whitening and why\nthe accuracy of BN degenerates with reduced batch sizes. We demonstrate the\nconsistently improved performance of IterNorm with extensive experiments on\nCIFAR-10 and ImageNet over BN and DBN.","url_abs":"http://arxiv.org/abs/1904.03441v1","url_pdf":"http://arxiv.org/pdf/1904.03441v1.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":"iterative-normalization-beyond","repo_url":"https://github.com/huangleiBuaa/IterNorm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"iterative-normalization-beyond","repo_url":"https://github.com/XingangPan/Switchable-Whitening","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"iterative-normalization-beyond","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":"iterative-normalization-beyond","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":"iterative-normalization-beyond","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":[{"task_slug":"robust-object-detection","task_name":"Robust Object Detection"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"decorrelated-batch-normalization","method_name":"Decorrelated Batch Normalization"},{"method_slug":"zca-whitening","method_name":"ZCA Whitening"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/robust-object-detection-on-dwd","task":"Robust Object Detection","dataset":"DWD","model":"IterNorm","rank_in_archive_order":12,"of":12,"metrics":{"mPC [AP50]":"23.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1904.03441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03441"}},"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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