Digit Classification on MNIST -> USPS (test)
98.5AccuracyDeepJDOT
Evaluation Results
| Method | Links | |
|---|---|---|
| DeepJDOTadaptation=source to target, eval_domain=source2018.03 | 98.5 | |
| Target Supervisedtraining=full supervised2019.09 | 96.3 | |
| 3CATN2019.09 | 96.1 | |
| UNIT2019.09 | 95.9 | |
| UNITadaptation=source to target2018.03 | 95.9 | |
| IntraDAArchitecture=LeNet variant2020.04 | 95.8 | |
| target onlyadaptation=none, train_domain=target2018.03 | 95.8 | |
| DeepJDOTBackbone=LeNet2021.07 | 95.7 | |
| DANNadaptation=source to target2018.03 | 95.7 | |
| DeepJDOTadaptation=source to target2018.03 | 95.7 | |
| CDANvariant=CDAN+E2019.09 | 95.6 | |
| CyCADAArchitecture=LeNet variant2020.04 | 95.6 | |
| GenToAdaptadaptation=source to target2018.03 | 95.3 | |
| PFAN2018.11 | 95 | |
| Fully SupervisedArchitecture=Simple CNN (2 conv, 2 fc, BN)2021.12 | 95 | |
| Source onlyadaptation=none, train_domain=source2018.03 | 94.8 | |
| SRDA-Gnoise_method=Gaussian, image_level_optimization=false2019.05 | 94.76 | |
| MCD2019.05 | 94.2 | |
| StochJDOTadaptation=source to target2018.03 | 93.6 | |
| SRDA-G*noise_method=Gaussian, image_level_optimization=true2019.05 | 93.25 | |
| MSTN2018.11 | 92.9 | |
| ADDAadaptation=source to target2018.03 | 92.4 | |
| I2I Adaptadaptation=source to target2018.03 | 92.1 | |
| DRCN2019.09 | 91.8 | |
| DRCN2019.05 | 91.8 | |
| DRCNadaptation=source to target2018.03 | 91.8 | |
| DSN2019.05 | 91.3 | |
| DSNadaptation=source to target2018.03 | 91.3 | |
| CoGAN2019.09 | 91.2 | |
| COGAN2019.05 | 91.2 | |
| CoGANadaptation=source to target2018.03 | 91.2 | |
| TOHANNumber of Target Data per Class=72021.06 | 90.4 | |
| TOHANNumber of Target Data per Class=62021.06 | 90 | |
| ADDA2018.11 | 89.4 | |
| ADDA2019.09 | 89.4 | |
| ADDAArchitecture=LeNet variant2020.04 | 89.4 | |
| TOHANNumber of Target Data per Class=52021.06 | 89.4 | |
| ADDA2019.05 | 89.4 | |
| DeepCORALadaptation=source to target2018.03 | 89.33 | |
| TOHANNumber of Target Data per Class=42021.06 | 89.3 | |
| RevGrad2019.09 | 89.1 | |
| TOHANNumber of Target Data per Class=32021.06 | 88.5 | |
| MMDadaptation=source to target2018.03 | 88.5 | |
| SRDA-V*noise_method=VAT, image_level_optimization=true2019.05 | 88.49 | |
| TOHANNumber of Target Data per Class=22021.06 | 88.3 | |
| Self-ensembleadaptation=source to target2018.03 | 88.14 | |
| TOHANNumber of Target Data per Class=12021.06 | 87.7 | |
| DUAArchitecture=Simple CNN (2 conv, 2 fc, BN)2021.12 | 86 | |
| SRDA-Fnoise_method=FGSM, image_level_optimization=false2019.05 | 85 | |
| SRDA-Vnoise_method=VAT, image_level_optimization=false2019.05 | 84.64 | |
| Source onlytraining=source only2019.09 | 82.2 | |
| Source onlyArchitecture=LeNet variant2020.04 | 82.2 | |
| dkdHTLNumber of Target Data per Class=72021.06 | 79 | |
| dkdHTLNumber of Target Data per Class=62021.06 | 78.8 | |
| dkdHTLNumber of Target Data per Class=52021.06 | 78.6 | |
| Source onlyArchitecture=Simple CNN (2 conv, 2 fc, BN)2021.12 | 78 | |
| dkdHTLNumber of Target Data per Class=42021.06 | 77.8 | |
| RevGrad2018.11 | 77.1 | |
| DANN2019.05 | 77.1 | |
| Source Onlyadaptation_protocol=No adaptation2019.05 | 76.7 | |
| Source Only2018.11 | 75.2 | |
| dkdHTLNumber of Target Data per Class=32021.06 | 74.4 | |
| dkdHTLNumber of Target Data per Class=22021.06 | 70.5 | |
| dkdHTLNumber of Target Data per Class=12021.06 | 65.2 | |
| SRDA-F*noise_method=FGSM, image_level_optimization=true2019.05 | 32.73 |