Domain Adaptation on USPS-to-MNIST

Metric: Accuracy (higher is better)

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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

#1FAMCD
98.75
Accuracy
No paper
#2FACTSOTA
98.6
Accuracy· 2023-06-01
FACT: Federated Adversarial Cross Training Code
#3SHOTSOTA
98.4
Accuracy· 2020-02-20
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation Code
#4CyCleGAN (Light-weight Calibrator)
98.3
Accuracy· 2019-11-28
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation Code
#53CATNSOTA
98.3
Accuracy· 2019-09-17
Cycle-consistent Conditional Adversarial Transfer Networks Code
#6Mean teacherSOTA
98.07
Accuracy· 2017-06-16
Self-ensembling for visual domain adaptation Code
#7CDANSOTA
98
Accuracy· 2017-05-26
Conditional Adversarial Domain Adaptation Code
#8DRANet
97.8
Accuracy· 2021-03-24
DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation Code
#9DFA-MCD
96.6
Accuracy· 2020-06-23
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment Code
#10DeepJDOT
96.4
Accuracy· 2018-03-27
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation Code
#11MCD+CAT
96.3
Accuracy· 2019-03-24
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation Code
#12DFA-ENT
96.2
Accuracy· 2020-06-23
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment Code
#13MCD
95.7
Accuracy· 2017-12-07
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation Code
#14SRDA (RAN)
95.03
Accuracy· 2019-05-26
Learning Smooth Representation for Unsupervised Domain Adaptation Code