| L2C (CLIP, ViT-L/14) |
- Average Accuracy
- 67.4
|
Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation 2025-01-15 |
코드 1 |
| PromptStyler (CLIP, ViT-L/14) |
- Average Accuracy
- 65.5
|
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27 |
코드 1 |
| VDPG (CLIP, ViT-L/14) |
- Average Accuracy
- 65.2
|
Adapting to Distribution Shift by Visual Domain Prompt Generation 2024-05-05 |
코드 1 |
| VL2V-SD (CLIP, ViT-B/16) |
- Average Accuracy
- 62.79
|
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification 2023-10-12 |
코드 1 |
| MoA (OpenCLIP, ViT-B/16) |
- Average Accuracy
- 62.7
|
Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters 2023-10-17 |
코드 1 |
| CAR-FT (CLIP, ViT-B/16) |
- Average Accuracy
- 62.5
|
Context-Aware Robust Fine-Tuning 2022-11-29 |
연결 없음 |
| SIMPLE+ |
- Average Accuracy
- 61.9
|
SIMPLE: Specialized Model-Sample Matching for Domain Generalization 2023-05-01 |
코드 1 |
| GMDG (RegNetY-16GF, SWAD) |
- Average Accuracy
- 61.3
|
Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29 |
코드 1 · 코드 2 |
| L2C (CLIP, ViT-B/16) |
- Average Accuracy
- 61.2
|
Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation 2025-01-15 |
코드 1 |
| Ensemble of Averages (RegNetY-16GF) |
- Average Accuracy
- 60.9
|
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21 |
코드 1 |
| MIRO (RegNetY-16GF, SWAD) |
- Average Accuracy
- 60.7
|
Domain Generalization by Mutual-Information Regularization with Pre-trained Models 2022-03-21 |
코드 1 |
| SPG (CLIP, ViT-B/16) |
- Average Accuracy
- 60.1
|
Soft Prompt Generation for Domain Generalization 2024-04-30 |
코드 1 |
| VDPG (CLIP, ViT-B/16) |
- Average Accuracy
- 59.8
|
Adapting to Distribution Shift by Visual Domain Prompt Generation 2024-05-05 |
코드 1 |
| UniDG + CORAL + ConvNeXt-B |
- Average Accuracy
- 59.5
|
Towards Unified and Effective Domain Generalization 2023-10-16 |
코드 1 |
| PromptStyler (CLIP, ViT-B/16) |
- Average Accuracy
- 59.4
|
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27 |
코드 1 |
| SEDGE+ |
- Average Accuracy
- 54.7
|
Domain Generalization using Pretrained Models without Fine-tuning 2022-03-09 |
연결 없음 |
| Ensemble of Averages (ResNeXt-50 32x4d) |
- Average Accuracy
- 54.6
|
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21 |
코드 1 |
| GMDG (RegNetY-16GF) |
- Average Accuracy
- 54.6
|
Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29 |
코드 1 · 코드 2 |
| Hybrid-SF-MoE |
- Average Accuracy
- 52.0
|
Sparse Mixture-of-Experts are Domain Generalizable Learners 2022-06-08 |
코드 1 · 코드 2 |
| CADG |
- Average Accuracy
- 51.6
|
CADG: A Model Based on Cross Attention for Domain Generalization 2022-03-31 |
연결 없음 |
| SPG (CLIP, ResNet-50) |
- Average Accuracy
- 50.1
|
Soft Prompt Generation for Domain Generalization 2024-04-30 |
코드 1 |
| PromptStyler (CLIP, ResNet-50) |
- Average Accuracy
- 49.5
|
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27 |
코드 1 |
| SIMPLE |
- Average Accuracy
- 49.2
|
SIMPLE: Specialized Model-Sample Matching for Domain Generalization 2023-05-01 |
코드 1 |
| GMoE-S/16 |
- Average Accuracy
- 48.7
|
Sparse Mixture-of-Experts are Domain Generalizable Learners 2022-06-08 |
코드 1 · 코드 2 |
| Ensemble of Averages (ResNet-50) |
- Average Accuracy
- 47.4
|
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21 |
코드 1 |
| GMDG (ResNet-50, SWAD) |
- Average Accuracy
- 47.3
|
Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29 |
코드 1 · 코드 2 |
| MIRO (ResNet-50, SWAD) |
- Average Accuracy
- 47.0
|
Domain Generalization by Mutual-Information Regularization with Pre-trained Models 2022-03-21 |
코드 1 |
| DDG |
- Average Accuracy
- 46.93
|
Dynamic Domain Generalization 2022-05-27 |
코드 1 |
| AdaClust (ResNet-50, SWAD) |
- Average Accuracy
- 46.7
|
Adaptive Methods for Aggregated Domain Generalization 2021-12-09 |
코드 1 |
| SWAD (ResNet-50) |
- Average Accuracy
- 46.5
|
SWAD: Domain Generalization by Seeking Flat Minima 2021-02-17 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |