الرئيسية الأخبار أحدث الأوراق البحثية الدروس مجموعات البيانات الموسوعة SOTA نماذج LLM لوحة الأداء GPU الفعاليات

العربية

Domain Adaptation On Mnist To Usps

المقاييس

Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	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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