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 1621–1630 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 | SIMPLE+ | Average Accuracy | 99 | — | Unverified |
| 2 | PromptStyler (CLIP, ViT-L/14) | Average Accuracy | 98.6 | — | Unverified |
| 3 | GMDG (RegNetY-16GF, SWAD) | Average Accuracy | 97.9 | — | Unverified |
| 4 | D-Triplet(RegNetY-16GF) | Average Accuracy | 97.6 | — | Unverified |
| 5 | MoA (OpenCLIP, ViT-B/16) | Average Accuracy | 97.4 | — | Unverified |
| 6 | GMDG (e RegNetY-16GF) | Average Accuracy | 97.3 | — | Unverified |
| 7 | PromptStyler (CLIP, ViT-B/16) | Average Accuracy | 97.2 | — | Unverified |
| 8 | SPG (CLIP, ViT-B/16) | Average Accuracy | 97 | — | Unverified |
| 9 | CAR-FT (CLIP, ViT-B/16) | Average Accuracy | 96.8 | — | Unverified |
| 10 | MIRO (RegNetY-16GF, SWAD) | Average Accuracy | 96.8 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ViT-8/B-224 | Accuracy - Clean Images | 450 | — | Unverified |
| 2 | VOLO-D5 | Accuracy - All Images | 57.2 | — | Unverified |
| 3 | ConvNeXt-B | Accuracy - All Images | 53.5 | — | Unverified |
| 4 | ResNeXt-101 32x16d | Accuracy - All Images | 51.7 | — | Unverified |
| 5 | EfficientNet-B8 (advprop+autoaug) | Accuracy - All Images | 50.5 | — | Unverified |
| 6 | EfficientNet-B7 (advprop+autoaug) | Accuracy - All Images | 49.7 | — | Unverified |
| 7 | EfficientNet-B6 (advprop+autoaug) | Accuracy - All Images | 49.6 | — | Unverified |
| 8 | EfficientNet-B5 (advprop+autoaug) | Accuracy - All Images | 49.1 | — | Unverified |
| 9 | ViT-16/L-224 | Accuracy - All Images | 49 | — | Unverified |
| 10 | ResNet-50 (gn) | Accuracy - All Images | 48.9 | — | Unverified |