Methods › General › Normalization › Weight Standardization
Weight Standardization
Introduced by Siyuan Qiao et al. in Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Weight Standardization is a normalization technique that smooths the loss landscape by standardizing the weights in convolutional layers. Different from the previous normalization methods that focus on activations, WS considers the smoothing effects of weights more than just length-direction decoupling. Theoretically, WS reduces the Lipschitz constants of the loss and the gradients. Hence, WS smooths the loss landscape and improves training.
In Weight Standardization, instead of directly optimizing the loss ℒ on the original weights Ŵ, we reparameterize the weights Ŵ as a function of W, i.e. Ŵ=WS(W), and optimize the loss ℒ on W by SGD:
Ŵ = [ Ŵ_(i,j) | Ŵ_(i,j) = W_(i,j) - μ_(W_(i,·))σ_(W_(i,·)+ϵ)]
y = Ŵ*x
where
μ_(W_(i,·)) = 1I∑ⱼ₌₁ᴵW_(i, j), σ_(W_(i,·))=√(1I∑ᵢ₌₁ᴵ(W_(i,j) - μ_(W_(i,·)))²)
Similar to Batch Normalization, WS controls the first and second moments of the weights of each output channel individually in convolutional layers. Note that many initialization methods also initialize the weights in some similar ways. Different from those methods, WS standardizes the weights in a differentiable way which aims to normalize gradients during back-propagation. Note that we do not have any affine transformation on Ŵ. This is because we assume that normalization layers such as BN or GN will normalize this convolutional layer again.
Papers archive 2025-07-28
12 shown of 12, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration 2 Oct 2024 · 0 repositories · arXiv:2410.02006
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Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks 24 Jul 2024 · 0 repositories · arXiv:2407.16944
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Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks 21 Sep 2023 · 1 repository
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Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks 26 May 2023 · 2 repositories · arXiv:2305.17212Syntology ran 0 of 6 samples · 6 unverified
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Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity 22 Oct 2021 · 0 repositories · arXiv:2110.11778
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Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images 31 May 2021 · 1 repository · arXiv:2106.00116
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ReCU: Reviving the Dead Weights in Binary Neural Networks 23 Mar 2021 · 3 repositories · arXiv:2103.12369Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
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Characterizing signal propagation to close the performance gap in unnormalized ResNets 21 Jan 2021 · 4 repositories · arXiv:2101.08692Syntology ran 1 of 1 samples · 0 unverified
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Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals 8 Jan 2021 · 1 repository
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Gradient Centralization: A New Optimization Technique for Deep Neural Networks 3 Apr 2020 · 8 repositories · arXiv:2004.01461Syntology ran 2 of 29 samples · 27 unverified · 1 pointer-only (licence)
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Big Transfer (BiT): General Visual Representation Learning 24 Dec 2019 · 9 repositories · arXiv:1912.11370Syntology ran 3 of 10 samples · 7 unverified
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Micro-Batch Training with Batch-Channel Normalization and Weight Standardization 25 Mar 2019 · 7 repositories · arXiv:1903.10520
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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