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Group Normalization

55 papers tagged archive 2025-07-28

Introduced by Yuxin Wu et al. in Group Normalization

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

Group Normalization is a normalization layer that divides channels into groups and normalizes the features within each group. GN does not exploit the batch dimension, and its computation is independent of batch sizes. In the case where the group size is 1, it is equivalent to Instance Normalization.

As motivation for the method, many classical features like SIFT and HOG had group-wise features and involved group-wise normalization. For example, a HOG vector is the outcome of several spatial cells where each cell is represented by a normalized orientation histogram.

Formally, Group Normalization is defined as:

μᵢ = 1/m∑_(k∈𝒮ᵢ)xₖ

σ²ᵢ = 1/m∑_(k∈𝒮ᵢ)(xₖ-μᵢ)²

x̂ᵢ = (xᵢ - μᵢ)/(√(σ²ᵢ+ϵ))

Here x is the feature computed by a layer, and i is an index. Formally, a Group Norm layer computes μ and σ in a set 𝒮ᵢ defined as: 𝒮ᵢ ={k |k_N = i_N ,⌊k_C/(C/G)⌋= ⌊I_C/(C/G)⌋}.

Here G is the number of groups, which is a pre-defined hyper-parameter (G = 32 by default). C/G is the number of channels per group. ⌊ is the floor operation, and the final term means that the indexes i and k are in the same group of channels, assuming each group of channels are stored in a sequential order along the C axis.

PaperSourceSee Code · pytorch/pytorch

Papers archive 2025-07-28

30 shown of 55, 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 72 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
Semantic Segmentation11
Image Classification10
Object Detection9
Segmentation9
Instance Segmentation8
object-detection6
image-classification5
Domain Adaptation4
Federated Learning4
Few-Shot Learning4
Image Segmentation4
Object4
Representation Learning4
Vocal Bursts Intensity Prediction4
Deep Learning3
GPU3
Image Generation3
Transfer Learning3
Clustering2
Fine-Grained Image Classification2

Usage over time archive 2025-07-28

Papers per year tagged with Group Normalization: 2017 to 2025, peak 14 14 0 2017: 1 paper 2017 2018: 4 papers 2018 2019: 14 papers 2019 2020: 13 papers 2020 2021: 9 papers 2021 2022: 4 papers 2022 2023: 4 papers 2023 2024: 5 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (55 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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