# mandalinadagi/WGSR

<https://repo.masters-masters.com/en/repos/mandalinadagi/WGSR>

[Home](https://repo.masters-masters.com/en) › [mandalinadagi](https://repo.masters-masters.com/en/owners/mandalinadagi) › [WGSR](https://repo.masters-masters.com/en/repos/mandalinadagi/WGSR)

mandalinadagi/WGSR has 101 stars on GitHub. Its RepoMasters card: Prototype tier. \[CVPR 2024\] Training Generative Image Super-Resolution Models by…

| Field | Value |
| --- | --- |
| Owner | [mandalinadagi](https://repo.masters-masters.com/en/owners/mandalinadagi) |
| Stars | 101 |
| Forks | 4 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2023 |
| Topics | adversarial-training, artifact-reduction, gan, super-resolution-image, wavelet-transform, computer-vision, generative-adversarial-network, super-resolution |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [mandalinadagi/WGSR](https://github.com/mandalinadagi/WGSR) |

## Repository

\[CVPR 2024\] Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.