| MoA (OpenCLIP, ViT-B/16) |
- Average Accuracy
- 90.6
|
Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters 2023-10-17 |
코드 1 |
| PromptStyler (CLIP, ViT-L/14) |
- Average Accuracy
- 89.1
|
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27 |
코드 1 |
| UniDG + CORAL + ConvNeXt-B |
- Average Accuracy
- 88.9
|
Towards Unified and Effective Domain Generalization 2023-10-16 |
코드 1 |
| SIMPLE+ |
- Average Accuracy
- 87.7
|
SIMPLE: Specialized Model-Sample Matching for Domain Generalization 2023-05-01 |
코드 1 |
| VL2V-SD (CLIP, ViT-B/16) |
- Average Accuracy
- 87.38
|
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification 2023-10-12 |
코드 1 |
| CAR-FT (CLIP, ViT-B/16) |
- Average Accuracy
- 85.7
|
Context-Aware Robust Fine-Tuning 2022-11-29 |
연결 없음 |
| GMDG (RegNetY-16GF, SWAD) |
- Average Accuracy
- 84.7
|
Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29 |
코드 1 · 코드 2 |
| SIMPLE |
- Average Accuracy
- 84.6
|
SIMPLE: Specialized Model-Sample Matching for Domain Generalization 2023-05-01 |
코드 1 |
| Ensemble of Averages (RegNetY-16GF) |
- Average Accuracy
- 83.9
|
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21 |
코드 1 |
| PromptStyler (CLIP, ViT-B/16) |
- Average Accuracy
- 83.6
|
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27 |
코드 1 |
| SPG (CLIP, ViT-B/16) |
- Average Accuracy
- 83.6
|
Soft Prompt Generation for Domain Generalization 2024-04-30 |
코드 1 |
| MIRO (RegNetY-16GF, SWAD) |
- Average Accuracy
- 83.3
|
Domain Generalization by Mutual-Information Regularization with Pre-trained Models 2022-03-21 |
코드 1 |
| D-Triplet(RegNetY-16GF) |
- Average Accuracy
- 82.6
|
Domain-aware Triplet loss in Domain Generalization 2023-03-01 |
코드 1 |
| GMDG (RegNetY-16GF) |
- Average Accuracy
- 80.8
|
Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29 |
코드 1 · 코드 2 |
| SEDGE+ |
- Average Accuracy
- 80.7
|
Domain Generalization using Pretrained Models without Fine-tuning 2022-03-09 |
연결 없음 |
| Ensemble of Averages (ResNeXt-50 32x4d) |
- Average Accuracy
- 80.2
|
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21 |
코드 1 |
| SEDGE |
- Average Accuracy
- 79.9
|
Domain Generalization using Pretrained Models without Fine-tuning 2022-03-09 |
연결 없음 |
| CADG |
- Average Accuracy
- 79.9
|
CADG: A Model Based on Cross Attention for Domain Generalization 2022-03-31 |
연결 없음 |
| GMoE-S/16 |
- Average Accuracy
- 74.2
|
Sparse Mixture-of-Experts are Domain Generalizable Learners 2022-06-08 |
코드 1 · 코드 2 |
| SPG (CLIP, ResNet-50) |
- Average Accuracy
- 73.8
|
Soft Prompt Generation for Domain Generalization 2024-04-30 |
코드 1 |
| PromptStyler (CLIP, ResNet-50) |
- Average Accuracy
- 73.6
|
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27 |
코드 1 |
| Model Ratatouille |
- Average Accuracy
- 73.5
|
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization 2022-12-20 |
코드 1 |
| Ensemble of Averages (ResNet-50) |
- Average Accuracy
- 72.5
|
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21 |
코드 1 |
| GMDG (ResNet-50, SWAD) |
- Average Accuracy
- 72.5
|
Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29 |
코드 1 · 코드 2 |
| MIRO (ResNet-50, SWAD) |
- Average Accuracy
- 72.4
|
Domain Generalization by Mutual-Information Regularization with Pre-trained Models 2022-03-21 |
코드 1 |
| DDG |
- Average Accuracy
- 72.31
|
Dynamic Domain Generalization 2022-05-27 |
코드 1 |
| PCL (swad+resnet50) |
- Average Accuracy
- 71.6
|
PCL: Proxy-Based Contrastive Learning for Domain Generalization 2022-01-01 |
코드 1 |
| VNE (ResNet-50, SWAD) |
- Average Accuracy
- 71.1
|
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution 2023-04-04 |
코드 1 |
| GMDG (ResNet-50) |
- Average Accuracy
- 70.7
|
Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29 |
코드 1 · 코드 2 |
| SWAD (ResNet-50) |
- Average Accuracy
- 70.6
|
SWAD: Domain Generalization by Seeking Flat Minima 2021-02-17 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |