Methods › General › Normalization › Attentive Normalization

Attentive Normalization

3 papers tagged archive 2025-07-28

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

Attentive Normalization generalizes the common affine transformation component in the vanilla feature normalization. Instead of learning a single affine transformation, AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformation applied to re-calibrate features in an instance-specific way. The weights are learned by leveraging feature attention.

Source: Attentive NormalizationSee Code · iVMCL/AttentiveNorm_Detection

Papers archive 2025-07-28

3 shown of 3, 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

12 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
Conditional Image Generation1
Image Classification1
Image Generation1
Instance Segmentation1
Object Detection1
Semantic Segmentation1
Semantic Similarity1
Semantic Textual Similarity1
Semantic correspondence1
Style Transfer1
Video Style Transfer1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with Attentive Normalization: 2019 to 2021, peak 1 1 0 2019: 1 paper 2019 2020: 1 paper 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (3 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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