Image-to-Image Translation
Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.
( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )
Papers
Showing 1–10 of 1184 papers
All datasetsSYNTHIA-to-CityscapesGTAV-to-Cityscapes LabelsCityscapes Labels-to-PhotoADE20K Labels-to-PhotosCOCO-Stuff Labels-to-PhotosADE20K-Outdoor Labels-to-PhotosCelebA-HQCityscapes-to-Foggy Cityscapescat2dogCityscapes Photo-to-LabelsBCIFLIR
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | MIC | mAP | 47.6 | — | Unverified |
| 2 | SSA-DA | mAP | 42.5 | — | Unverified |
| 3 | MCAR | mAP | 38.8 | — | Unverified |
| 4 | Progressive Domain Adaptation | mAP | 36.9 | — | Unverified |
| 5 | Diversify & Match | mAP | 34.6 | — | Unverified |
| 6 | FRCNN in the wild | mAP | 27.6 | — | Unverified |