Domain Adaptation on SVHN to MNIST (test) benchmark leaderboard · SOTA2 Research

Domain Adaptation on SVHN to MNIST (test)

98.8AccuracyrRevGrad+CAT

Evaluation Results

Method	Links
rRevGrad+CAT2019.03		98.8
CAT2019.03		98.1
RevGrad+CAT2019.03		98
DWT2019.09		97.7
ADA2019.09		97.6
AssocDA2019.03		97.6
MCD+CAT2019.03		97.1
MCD2019.03		96.2
Inference-time Label-Preserving Target Projections2021.03		95.33
MEGA2019.09		95.2
VADA+CAT2019.03		95.2
CCSA2021.03		94.57
VADA2019.03		94.5
Inference-time Label-Preserving Target Projections2021.03		92.02
M-ADA2021.03		91.81
JAE2017.05		91.8
MSTN2019.03		91.7
M-ADA2021.03		89.9
AutoDIAL2019.09		89.12
FADA2021.03		87.2
Asymmetric Tri-training2017.02		86.2
ATT2019.09		86.2
Asymmetric Tri-training2017.02		85
DSN2017.02		82.7
DRCN2017.02		82
DRCN2019.09		82
DRCN2019.03		82
LEL2019.03		81
JAE2017.05		80.5
Asymmetric Tri-training2017.02		79.8
DTN2016.11		79.72
KNN-Ad2017.02		78.8
SBADA-GAN2019.09		76.1
ADDA2019.09		76
ADDA2019.03		76
ADDA2017.05		76
GAM2019.09		74.6
DANN2019.09		73.9
RevGrad2019.03		73.9
Gradient reversal2017.05		73.9
DANN2016.11		73.85
Shape-Biased Framework2019.09		71.7
MMD2017.02		71.1
DANN2017.02		71.1
Source Only with BN2017.02		70.1
Source Only w/o BN2017.02		68.1
DDC2019.03		68.1
Domain confusion2017.05		68.1
Inference-time Label-Preserving Target Projections2021.03		64.12
M-ADA2021.03		60.14
Source Only2019.03		60.1
SA2016.11		59.32
JiGen2019.09		57.6