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StyleGAN

292 papers tagged archive 2025-07-28

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

StyleGAN is a type of generative adversarial network. It uses an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature; in particular, the use of adaptive instance normalization. Otherwise it follows Progressive GAN in using a progressively growing training regime. Other quirks include the fact it generates from a fixed value tensor not stochastically generated latent variables as in regular GANs. The stochastically generated latent variables are used as style vectors in the adaptive instance normalization at each resolution after being transformed by an 8-layer feedforward network. Lastly, it employs a form of regularization called mixing regularization, which mixes two style latent variables during training.

Source: A Style-Based Generator Architecture for Generative...

Papers archive 2025-07-28

30 shown of 292, 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 172 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 Generation71
Attribute35
Disentanglement29
Face Generation18
Image Manipulation17
Face Recognition14
Face Swapping14
Decoder12
Diversity11
Generative Adversarial Network10
Image-to-Image Translation10
Super-Resolution10
Video Generation10
Data Augmentation9
Style Transfer8
Transfer Learning8
Facial Editing7
Domain Adaptation6
Face Verification6
Representation Learning6

Usage over time archive 2025-07-28

Papers per year tagged with StyleGAN: 2018 to 2025, peak 94 94 0 2018: 1 paper 2018 2019: 13 papers 2019 2020: 37 papers 2020 2021: 19 papers 2021 2022: 71 papers 2022 2023: 94 papers 2023 2024: 50 papers 2024 2025: 7 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (292 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 Models

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