Methods › Computer Vision › Generative Models › Vision-aided GAN

Vision-aided GAN

1 paper tagged archive 2025-07-28

Introduced by Nupur Kumari et al. in Ensembling Off-the-shelf Models for GAN Training

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

Vision-aided GAN training involves using pretrained computer vision models in an ensemble of discriminators to improve GAN performance. Linear separability between real and fake samples in pretrained model embeddings is used as a measure to choose the most accurate pretrained models for a dataset.

PaperSourceSee Code · nupurkmr9/vision-aided-gan

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

1 task 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 Generation1

Usage over time archive 2025-07-28

Papers per year tagged with Vision-aided GAN: 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 Models

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