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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 Mnist To Usps

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title	Repository
3CATN	96.1	Cycle-consistent Conditional Adversarial Transfer Networks
SHOT	98.0	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
rRevGrad+CAT	96	Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
FAMCD	98.72	Unsupervised domain adaptation using feature aligned maximum classifier discrepancy	-
CyCleGAN (Light-weight Calibrator)	97.1	Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	-
DRANet	98.2	DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation
Mean teacher	98.26	Self-ensembling for visual domain adaptation
DeepJDOT	95.7	DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
FACT	98.8	FACT: Federated Adversarial Cross Training
DFA-ENT	97.9	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
MCD	93.8	Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
ADDN	90.1	Adversarial Discriminative Domain Adaptation
DFA-MCD	98.6	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
SRDA (RAN)	94.76	Learning Smooth Representation for Unsupervised Domain Adaptation

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Domain Adaptation On Mnist To Usps | SOTA | HyperAI초신경