Domain Generalization
The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
Papers
Showing 1–10 of 1751 papers
All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Self-adaptation (ResNet - 101) | mIoU | 4,489 | — | Unverified |
| 2 | Self-adaptation (ResNet - 50) | mIoU | 4,407 | — | Unverified |
| 3 | SoRA | mIoU | 68.27 | — | Unverified |
| 4 | MFuser | mIoU | 68.2 | — | Unverified |
| 5 | tqdm (EVA02-CLIP-L) | mIoU | 66.05 | — | Unverified |
| 6 | ADSI | mIoU | 65.57 | — | Unverified |
| 7 | Rein | mIoU | 64.3 | — | Unverified |
| 8 | VLTSeg | mIoU | 63.5 | — | Unverified |
| 9 | CLOUDS | mIoU | 61.5 | — | Unverified |
| 10 | DIDEX | mIoU | 59.7 | — | Unverified |