Domain Adaptation on USPS-to-MNIST

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Augmentations	Paper	Date↕	Code
1	FAMCD	98.75	No	-	-	-
2	FACT	98.6	No	FACT: Federated Adversarial Cross Training	2023-06-01	Code
3	SHOT	98.4	No	Do We Really Need to Access the Source Data? Sou...	2020-02-20	Code
4	CyCleGAN (Light-weight Calibrator)	98.3	No	Light-weight Calibrator: a Separable Component f...	2019-11-28	Code
5	3CATN	98.3	No	Cycle-consistent Conditional Adversarial Transfe...	2019-09-17	Code
6	Mean teacher	98.07	No	Self-ensembling for visual domain adaptation	2017-06-16	Code
7	CDAN	98	No	Conditional Adversarial Domain Adaptation	2017-05-26	Code
8	DRANet	97.8	No	DRANet: Disentangling Representation and Adaptat...	2021-03-24	Code
9	DFA-MCD	96.6	No	Discriminative Feature Alignment: Improving Tran...	2020-06-23	Code
10	DeepJDOT	96.4	No	DeepJDOT: Deep Joint Distribution Optimal Transp...	2018-03-27	Code
11	MCD+CAT	96.3	No	Cluster Alignment with a Teacher for Unsupervise...	2019-03-24	Code
12	DFA-ENT	96.2	No	Discriminative Feature Alignment: Improving Tran...	2020-06-23	Code
13	MCD	95.7	No	Maximum Classifier Discrepancy for Unsupervised ...	2017-12-07	Code
14	SRDA (RAN)	95.03	No	Learning Smooth Representation for Unsupervised ...	2019-05-26	Code