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 | MoA (OpenCLIP, ViT-B/16) | Average Accuracy | 90.6 | — | Unverified |
| 2 | PromptStyler (CLIP, ViT-L/14) | Average Accuracy | 89.1 | — | Unverified |
| 3 | UniDG + CORAL + ConvNeXt-B | Average Accuracy | 88.9 | — | Unverified |
| 4 | SIMPLE+ | Average Accuracy | 87.7 | — | Unverified |
| 5 | VL2V-SD (CLIP, ViT-B/16) | Average Accuracy | 87.38 | — | Unverified |
| 6 | CAR-FT (CLIP, ViT-B/16) | Average Accuracy | 85.7 | — | Unverified |
| 7 | GMDG (RegNetY-16GF, SWAD) | Average Accuracy | 84.7 | — | Unverified |
| 8 | SIMPLE | Average Accuracy | 84.6 | — | Unverified |
| 9 | Ensemble of Averages (RegNetY-16GF) | Average Accuracy | 83.9 | — | Unverified |
| 10 | SPG (CLIP, ViT-B/16) | Average Accuracy | 83.6 | — | Unverified |