Methods › Computer Vision › Generative Adversarial Networks › CS-GAN
CS-GAN
Introduced by Yan Wu et al. in Deep Compressed Sensing
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
CS-GAN is a type of generative adversarial network that uses a form of deep compressed sensing, and latent optimisation, to improve the quality of generated samples.
Papers archive 2025-07-28
3 shown of 3, 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.
-
Edge-based fever screening system over private 5G 8 Feb 2022 · 0 repositories · arXiv:2202.03917
-
LOGAN: Latent Optimisation for Generative Adversarial Networks 2 Dec 2019 · 1 repository · arXiv:1912.00953Syntology ran 2 of 8 samples · 6 unverified
-
Deep Compressed Sensing 16 May 2019 · 1 repository · arXiv:1905.06723
Tasks archive 2025-07-28
6 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 |
|---|---|
| Conditional Image Generation | 1 |
| Edge-computing | 1 |
| Generative Adversarial Network | 1 |
| Image Generation | 1 |
| Meta-Learning | 1 |
| compressed sensing | 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