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 | Mixer-B/8-SAM | Top-1 Error Rate | 76.5 | — | Unverified |
| 2 | ViT-B/16-SAM | Top-1 Error Rate | 73.6 | — | Unverified |
| 3 | ResNet-152x2-SAM | Top-1 Error Rate | 71.9 | — | Unverified |
| 4 | ResNet-50 | Top-1 Error Rate | 63.9 | — | Unverified |
| 5 | AugMix (ResNet-50) | Top-1 Error Rate | 58.9 | — | Unverified |
| 6 | Stylized ImageNet (ResNet-50) | Top-1 Error Rate | 58.5 | — | Unverified |
| 7 | DeepAugment (ResNet-50) | Top-1 Error Rate | 57.8 | — | Unverified |
| 8 | PRIME (ResNet-50) | Top-1 Error Rate | 57.1 | — | Unverified |
| 9 | RVT-Ti* | Top-1 Error Rate | 56.1 | — | Unverified |
| 10 | PRIME with JSD (ResNet-50) | Top-1 Error Rate | 53.7 | — | Unverified |