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Spectral Normalization

220 papers tagged archive 2025-07-28

Introduced by Takeru Miyato et al. in Spectral Normalization for Generative Adversarial Networks

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

Spectral Normalization is a normalization technique used for generative adversarial networks, used to stabilize training of the discriminator. Spectral normalization has the convenient property that the Lipschitz constant is the only hyper-parameter to be tuned.

It controls the Lipschitz constant of the discriminator f by constraining the spectral norm of each layer g : hᵢₙ →hₒᵤₜ. The Lipschitz norm ‖g‖_(Lip) is equal to supₕσ(∇g(h)), where σ(a) is the spectral norm of the matrix A (L₂ matrix norm of A):

σ(a) = max_(h:h≠0)(‖Ah‖₂)/(‖h‖₂) = max_(‖h‖₂≤1)‖Ah‖₂

which is equivalent to the largest singular value of A. Therefore for a linear layer g(h) = Wh the norm is given by ‖g‖_(Lip) = supₕσ(∇g(h)) = supₕσ(W) = σ(W). Spectral normalization normalizes the spectral norm of the weight matrix W so it satisfies the Lipschitz constraint σ(W) = 1:

W̅_(SN)(W) = W / σ(W)

PaperSourceSee Code · christiancosgrove/pytorch-spectral-normalization-gan

Papers archive 2025-07-28

30 shown of 220, 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 201 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 Generation57
Conditional Image Generation20
Generative Adversarial Network19
reinforcement-learning17
Reinforcement Learning13
Translation13
Reinforcement Learning (RL)12
Image-to-Image Translation11
Data Augmentation10
Multi-agent Reinforcement Learning9
Super-Resolution9
Image Classification7
Transfer Learning7
Unconditional Image Generation7
Vocal Bursts Intensity Prediction7
image-classification7
Attribute6
Deep Reinforcement Learning6
Decision Making5
Denoising5

Usage over time archive 2025-07-28

Papers per year tagged with Spectral Normalization: 2018 to 2025, peak 48 48 0 2018: 14 papers 2018 2019: 22 papers 2019 2020: 48 papers 2020 2021: 37 papers 2021 2022: 41 papers 2022 2023: 43 papers 2023 2024: 12 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (220 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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