Methods › Computer Vision › Generative Adversarial Networks › StyleMapGAN

StyleMapGAN

1 paper tagged archive 2025-07-28

Introduced by Hyunsu Kim et al. in Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

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

StyleMapGAN is a generative adversarial network for real-time image editing. The intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It aims to make the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs.

PaperSource

Papers archive 2025-07-28

1 shown of 1, 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

2 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 Manipulation1
valid1

Usage over time archive 2025-07-28

Papers per year tagged with StyleMapGAN: 2021 to 2021, peak 1 1 0 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (1 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 Networks

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