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

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Domain Adaptation On Svhn To Mnist

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title	Repository
ADDN	80.1	Adversarial Discriminative Domain Adaptation	-
CYCADA	90.4	CyCADA: Cycle-Consistent Adversarial Domain Adaptation	-
Mean teacher	99.18	Self-ensembling for visual domain adaptation	-
DFA-MCD	98.9	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment	-
DFA-ENT	98.2	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment	-
CDAN	89.2	Conditional Adversarial Domain Adaptation	-
CyCleGAN (Light-weight Calibrator)	97.5	Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	-
SBADA	76.1	From source to target and back: symmetric bi-directional adaptive GAN	-
FAMCD	98.76	Unsupervised domain adaptation using feature aligned maximum classifier discrepancy	-
SHOT	98.9	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	-
MCD	95.8	Maximum Classifier Discrepancy for Unsupervised Domain Adaptation	-
FACT	90.6	FACT: Federated Adversarial Cross Training	-
MSTN	93.3	Learning Semantic Representations for Unsupervised Domain Adaptation
PFA	93.9	Progressive Feature Alignment for Unsupervised Domain Adaptation	-

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Domain Adaptation On Svhn To Mnist | SOTA | HyperAI초신경