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 | ResNet-50 | mean Corruption Error (mCE) | 76.7 | — | Unverified |
| 2 | Group-wise Inhibition (ResNet-50) | mean Corruption Error (mCE) | 69.6 | — | Unverified |
| 3 | Stylized ImageNet (ResNet-50) | mean Corruption Error (mCE) | 69.3 | — | Unverified |
| 4 | AugMix (ResNet-50) | mean Corruption Error (mCE) | 65.3 | — | Unverified |
| 5 | APR-SP (ResNet-50) | mean Corruption Error (mCE) | 65 | — | Unverified |
| 6 | DiffAUD (ConvNeXt-Tiny) | Top 1 Accuracy | 64.3 | — | Unverified |
| 7 | DiffAUD (Swin-Tiny) | Top 1 Accuracy | 61 | — | Unverified |
| 8 | DeepAugment (ResNet-50) | mean Corruption Error (mCE) | 60.4 | — | Unverified |
| 9 | PRIME (ResNet-50) | mean Corruption Error (mCE) | 57.5 | — | Unverified |
| 10 | APR-SP + DeepAugment (ResNet-50) | mean Corruption Error (mCE) | 57.5 | — | Unverified |