Methods › Computer Vision › Generative Adversarial Networks › Anycost GAN
Anycost GAN
Introduced by Ji Lin et al. in Anycost GANs for Interactive Image Synthesis and Editing
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Anycost GAN is a type of generative adversarial network for image synthesis and editing. Given an input image, we project it into the latent space with encoder E and backward optimization. We can modify the latent code with user input to edit the image. During editing, a sub-generator of small cost is used for fast and interactive preview; during idle time, the full cost generator renders the final, high-quality output. The outputs from the full and sub-generators are visually consistent during projection and editing.
Papers archive 2025-07-28
2 shown of 2, 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.
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Anycost GANs for Interactive Image Synthesis and Editing 4 Mar 2021 · 1 repository · arXiv:2103.03243Syntology ran 0 of 1 samples · 1 unverified
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Interpretable Multiple Treatment Revenue Uplift Modeling 9 Jan 2021 · 0 repositories · arXiv:2101.03336
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Decision Making | 1 |
| Image Generation | 1 |
| Marketing | 1 |
Usage over time archive 2025-07-28
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections