Methods › General › Normalization › Gradient Normalization

Gradient Normalization

15 papers tagged archive 2025-07-28

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

Gradient Normalization is a normalization method for Generative Adversarial Networks to tackle the training instability of generative adversarial networks caused by the sharp gradient space. Unlike existing work such as gradient penalty and spectral normalization, the proposed GN only imposes a hard 1-Lipschitz constraint on the discriminator function, which increases the capacity of the network.

Source: Gradient Normalization for Generative Adversarial Networks

Papers archive 2025-07-28

15 shown of 15, 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

14 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 Classification2
Image Generation2
image-classification2
All1
Binary Classification1
Deep Learning1
Domain Adaptation1
Federated Learning1
MRI Reconstruction1
Multi-Task Learning1
Stochastic Optimization1
Test-time Adaptation1
Vertical Federated Learning1
Visual Localization1

Usage over time archive 2025-07-28

Papers per year tagged with Gradient Normalization: 2021 to 2025, peak 5 5 0 2021: 3 papers 2021 2022: 1 paper 2022 2023: 4 papers 2023 2024: 5 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (15 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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