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아카이브 평가 45건 · 마지막 적재/변경

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원본 기록 순서 · 순위 미부여
모델원본 지표·값논문구현
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