Title: [2503.10622] Transformers without Normalization
Description: Abstract page for arXiv paper 2503.10622: Transformers without Normalization
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Text content: [2503.10622] Transformers without Normalization                                         Skip to main content      We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate       > cs > arXiv:2503.10622         Help | Advanced Search     All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text     Search                open search       GO    open navigation menu   quick links  Login Help Pages About             Computer Science > Machine Learning   arXiv:2503.10622 (cs)            [Submitted on 13 Mar 2025] Title:Transformers without Normalization Authors:Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, Zhuang Liu View a PDF of the paper titled Transformers without Normalization, by Jiachen Zhu and 4 other authors View PDF HTML (experimental)  Abstract:Normalization layers are ubiquitous in modern neural networks and have long been considered essential. This work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple technique. We introduce Dynamic Tanh (DyT), an element-wise operation $DyT($x$) = \tanh(\alpha $x$)$, as a drop-in replacement for normalization layers in Transformers. DyT is inspired by the observation that layer normalization in Transformers often produces tanh-like, $S$-shaped input-output mappings. By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning. We validate the effectiveness of Transformers with DyT across diverse settings, ranging from recognition to generation, supervised to self-supervised learning, and computer vision to language models. These findings challenge the conventional understanding that normalization layers are indispensable in modern neural networks, and offer new insights into their role in deep networks.          Comments: CVPR 2025; Project page: this https URL   Subjects:  Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)  Cite as: arXiv:2503.10622 [cs.LG]     (or  arXiv:2503.10622v1 [cs.LG] for this version)                 https://doi.org/10.48550/arXiv.2503.10622   Focus to learn more                      arXiv-issued DOI via DataCite (pending registration)        Submission history From: Zhuang Liu [view email]       [v1]         Thu, 13 Mar 2025 17:59:06 UTC (2,285 KB)       Full-text links: Access Paper:   View a PDF of the paper titled Transformers without Normalization, by Jiachen Zhu and 4 other authorsView PDFHTML (experimental)TeX SourceOther Formats view license        Current browse context: cs.LG   < prev    |    next >   new  |  recent  | 2025-03      Change to browse by:      cs cs.AI cs.CL cs.CV     References & Citations  NASA ADSGoogle Scholar Semantic Scholar     a export BibTeX citation Loading...     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