Domain Adaptation On Usps To Mnist

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
FAMCD	98.75	Unsupervised domain adaptation using feature aligned maximum classifier discrepancy
FACT	98.6	FACT: Federated Adversarial Cross Training
SHOT	98.4	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
CyCleGAN (Light-weight Calibrator)	98.3	Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
3CATN	98.3	Cycle-consistent Conditional Adversarial Transfer Networks
Mean teacher	98.07	Self-ensembling for visual domain adaptation
CDAN	98.0	Conditional Adversarial Domain Adaptation
DRANet	97.8	DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation
DFA-MCD	96.6	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
DeepJDOT	96.4	DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
MCD+CAT	96.3	Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
DFA-ENT	96.2	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
MCD	95.7	Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
SRDA (RAN)	95.03	Learning Smooth Representation for Unsupervised Domain Adaptation

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Domain Adaptation On Usps To Mnist | SOTA | HyperAI