Methods › General › Normalization › Activation Normalization

Activation Normalization

61 papers tagged archive 2025-07-28

Introduced by Diederik P. Kingma et al. in Glow: Generative Flow with Invertible 1x1 Convolutions

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Activation Normalization is a type of normalization used for flow-based generative models; specifically it was introduced in the GLOW architecture. An ActNorm layer performs an affine transformation of the activations using a scale and bias parameter per channel, similar to batch normalization. These parameters are initialized such that the post-actnorm activations per-channel have zero mean and unit variance given an initial minibatch of data. This is a form of data dependent initilization. After initialization, the scale and bias are treated as regular trainable parameters that are independent of the data.

PaperSourceSee Code · openai/glow

Papers archive 2025-07-28

30 shown of 61, 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.

Tasks archive 2025-07-28

20 shown of 86 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Generation7
Text to Speech6
text-to-speech6
Anomaly Detection3
Density Estimation3
Flare Removal3
Image Enhancement3
Out of Distribution (OOD) Detection3
Unsupervised Anomaly Detection3
Attribute2
BIG-bench Machine Learning2
Dimensionality Reduction2
Federated Learning2
Image Dehazing2
Low-Light Image Enhancement2
Out-of-Distribution Detection2
Text-To-Speech Synthesis2
Variational Inference2
Zero-Shot Learning2
Adversarial Robustness1

Usage over time archive 2025-07-28

Papers per year tagged with Activation Normalization: 2018 to 2025, peak 11 11 0 2018: 1 paper 2018 2019: 9 papers 2019 2020: 10 papers 2020 2021: 11 papers 2021 2022: 6 papers 2022 2023: 9 papers 2023 2024: 11 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (61 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Normalization

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