Methods › General › Normalization › Instance-Level Meta Normalization
Instance-Level Meta Normalization
Introduced by Songhao Jia et al. in Instance-Level Meta Normalization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Instance-Level Meta Normalization is a normalization method that addresses a learning-to-normalize problem. ILM-Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. It uses an auto-encoder to predict the weights ω and bias β as the rescaling parameters for recovering the distribution of the tensor x of feature maps. Instead of using the entire feature tensor x as the input for the auto-encoder, it uses the mean μ and variance γ of x for characterizing its statistics.
Papers archive 2025-07-28
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Instance-Level Meta Normalization 6 Apr 2019 · 1 repository · arXiv:1904.03516
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Categories archive 2025-07-28
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