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아카이브 평가 133건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
SIMPLE+	Average Accuracy 99.0	SIMPLE: Specialized Model-Sample Matching for Domain Generalization 2023-05-01	코드 1
PromptStyler (CLIP, ViT-L/14)	Average Accuracy 98.6	PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27	코드 1
GMDG (RegNetY-16GF, SWAD)	Average Accuracy 97.9	Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29	코드 1 · 코드 2
D-Triplet(RegNetY-16GF)	Average Accuracy 97.6	Domain-aware Triplet loss in Domain Generalization 2023-03-01	코드 1
MoA (OpenCLIP, ViT-B/16)	Average Accuracy 97.4	Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters 2023-10-17	코드 1
GMDG (e RegNetY-16GF)	Average Accuracy 97.3	Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29	코드 1 · 코드 2
PromptStyler (CLIP, ViT-B/16)	Average Accuracy 97.2	PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27	코드 1
SPG (CLIP, ViT-B/16)	Average Accuracy 97.0	Soft Prompt Generation for Domain Generalization 2024-04-30	코드 1
MIRO (RegNetY-16GF, SWAD)	Average Accuracy 96.8	Domain Generalization by Mutual-Information Regularization with Pre-trained Models 2022-03-21	코드 1
CAR-FT (CLIP, ViT-B/16)	Average Accuracy 96.8	Context-Aware Robust Fine-Tuning 2022-11-29	연결 없음
VL2V-SD (CLIP, ViT-B/16)	Average Accuracy 96.68	Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification 2023-10-12	코드 1
SEDGE+	Average Accuracy 96.1	Domain Generalization using Pretrained Models without Fine-tuning 2022-03-09	연결 없음
Ensemble of Averages (RegNetY-16GF)	Average Accuracy 95.8	Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21	코드 1
UniDG + CORAL + ConvNeXt-B	Average Accuracy 95.6	Towards Unified and Effective Domain Generalization 2023-10-16	코드 1
CADG	Average Accuracy 94.6	CADG: A Model Based on Cross Attention for Domain Generalization 2022-03-31	연결 없음
Ensemble of Averages (ResNeXt-50 32x4d)	Average Accuracy 93.2	Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21	코드 1
PromptStyler (CLIP, ResNet-50)	Average Accuracy 93.2	PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization 2023-07-27	코드 1
SPG (CLIP, ResNet-50)	Average Accuracy 92.8	Soft Prompt Generation for Domain Generalization 2024-04-30	코드 1
EoQ (ResNet-50)	Average Accuracy 90.7	QT-DoG: Quantization-aware Training for Domain Generalization 2024-10-08	코드 1
Model Ratatouille	Average Accuracy 90.5	Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization 2022-12-20	코드 1
RISE (ResNet-50)	Average Accuracy 90.2	A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance 2023-09-21	코드 1
AdaClust (ResNet-50, SWAD)	Average Accuracy 89.2	Adaptive Methods for Aggregated Domain Generalization 2021-12-09	코드 1
PCL (ResNet50, SWAD)	Average Accuracy 88.7	PCL: Proxy-Based Contrastive Learning for Domain Generalization 2022-01-01	코드 1
LRDG (ResNet-50)	Average Accuracy 88.63	Domain Generalization by Learning and Removing Domain-specific Features 2022-12-14	코드 1
Ensemble of Averages (ResNet-50)	Average Accuracy 88.6	Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization 2021-10-21	코드 1
BatchFormer(ResNet-50, SWAD)	Average Accuracy 88.6	BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning 2022-03-03	코드 1
SIMPLE	Average Accuracy 88.6	SIMPLE: Specialized Model-Sample Matching for Domain Generalization 2023-05-01	코드 1
MIRO (ResNet-50, SWAD)	Average Accuracy 88.4	Domain Generalization by Mutual-Information Regularization with Pre-trained Models 2022-03-21	코드 1
GMDG (ResNet-50, SWAD)	Average Accuracy 88.4	Rethinking Multi-domain Generalization with A General Learning Objective 2024-02-29	코드 1 · 코드 2
VNE (ResNet-50, SWAD)	Average Accuracy 88.3	VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution 2023-04-04	코드 1