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Domain Adaptation On Svhn To Mnist

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	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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