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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Source Free Domain Adaptation On Visda 2017

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title	Repository
SFDA2++	89.6	SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation	-
RCL	93.2	Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning	-
NRC	85.9	Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation	-
G-SFDA	85.4	Generalized Source-free Domain Adaptation	-
SHOT++	87.3	Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer	-
SHOT	82.9	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	-
SFDA2	88.1	SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation	-
DaC	87.3	Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning	-
C-SFDA	87.8	C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation	-

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Source Free Domain Adaptation On Visda 2017 | SOTA | HyperAI초신경