3260 papers • 126 benchmarks • 313 datasets
This task has no description! Would you like to contribute one?
(Image credit: Papersgraph)
These leaderboards are used to track progress in reverse-style-transfer
No benchmarks available.
Use these libraries to find reverse-style-transfer models and implementations
No datasets available.
No subtasks available.
Instagram Filter Removal Network (IFRNet) is introduced to mitigate the effects of image filters for social media analysis applications and outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent.
This work introduces Contrastive Instagram Filter Removal Network (CIFR), which enhances this idea for Instagram filter removal by employing a novel multi-layer patch-wise contrastive style learning mechanism.
Two approaches proposed: a two-stage model and an end-to-end model are proposed that are able to not only generate stylized images of quality comparable with the ones produced by typical style transfer methods, but also effectively eliminate the artifacts introduced in reconstructing original input from a stylized image.
Adding a benchmark result helps the community track progress.