Methods › Computer Vision › Generative Adversarial Networks › SNGAN

Spectrally Normalised GAN

SNGAN

12 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.

SNGAN, or Spectrally Normalised GAN, is a type of generative adversarial network that uses spectral normalization, a type of weight normalization, to stabilise the training of the discriminator.

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

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

13 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 Generation6
Conditional Image Generation2
Unconditional Image Generation2
Data Augmentation1
Diversity1
Generative Adversarial Network1
Meta-Learning1
Object1
Object Detection1
Transfer Learning1
Vocal Bursts Intensity Prediction1
compressed sensing1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with SNGAN: 2018 to 2023, peak 6 6 0 2018: 1 paper 2018 2019: 2 papers 2019 2020: 6 papers 2020 2021: 2 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
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

Generative Adversarial NetworksGenerative Models

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