Methods › General › Feedforward Networks › Affine Operator

Affine Operator

12 papers tagged archive 2025-07-28

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

The Affine Operator is an affine transformation layer introduced in the ResMLP architecture. This replaces layer normalization, as in Transformer based networks, which is possible since in the ResMLP, there are no self-attention layers which makes training more stable - hence allowing a more simple affine transformation.

The affine operator is defined as:

Aff_(α, β)(𝐱)=Diag(α) 𝐱+β

where α and β are learnable weight vectors. This operation only rescales and shifts the input element-wise. This operation has several advantages over other normalization operations: first, as opposed to Layer Normalization, it has no cost at inference time, since it can absorbed in the adjacent linear layer. Second, as opposed to BatchNorm and Layer Normalization, the Aff operator does not depend on batch statistics.

Source: ResMLP: Feedforward networks for image classification...

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.

Tasks archive 2025-07-28

20 shown of 26 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 Classification4
Segmentation3
Semantic Segmentation3
image-classification3
Object Detection2
Translation2
object-detection2
Adversarial Attack1
Color Manipulation1
Data Augmentation1
Fine-Grained Image Classification1
GPU1
Gaussian Processes1
General Classification1
Image Enhancement1
Image Segmentation1
Inductive Bias1
Instance Segmentation1
Machine Translation1
Medical Image Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with Affine Operator: 2021 to 2024, peak 4 4 0 2021: 4 papers 2021 2022: 3 papers 2022 2023: 3 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (12 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

Feedforward Networks

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