Image Classification on CIFAR-10 4,000 labels (test)
2.7Test Error RateUDA (RandAugment)
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| UDA (RandAugment)Model=PyramidNet, # Param=26M2019.04 | 2.7 | — | |
| 100% SupervisedBackbone=Shake-Shake2019.08 | 2.86 | — | |
| UDA (RandAugment)Model=Shake-Shake, # Param=26M2019.04 | 3.7 | — | |
| FixMatch + IAM-Dlabeled samples=40002026.05 | 3.88 | — | |
| FixMatchlabeled samples=40002026.05 | 4.1 | — | |
| FixMatch + CMW-Net2022.02 | 4.25 | — | |
| FixMatch2022.02 | 4.26 | — | |
| UDA (RandAugment)Model=WRN-28-2, # Param=1.5M2019.04 | 4.32 | — | |
| UDA2022.02 | 4.88 | — | |
| MixMatchNumber of labeled samples=4000, Model parameters=26M2019.05 | 4.95 | — | |
| MixMatchModel=WRN, # Param=26M2019.04 | 4.95 | — | |
| SWANumber of labeled samples=4000, Model parameters=26M2019.05 | 5 | — | |
| Fast-SWAModel=Shake-Shake, # Param=26M2019.04 | 5 | — | |
| HybridNetBackbone=Shake-Shake2019.08 | 6.09 | — | |
| MixMatchModel=WRN-28-2, # Param=1.5M2019.04 | 6.24 | — | |
| Mean TeacherNumber of labeled samples=4000, Model parameters=26M2019.05 | 6.28 | — | |
| Mean TeacherModel=Shake-Shake, # Param=26M2019.04 | 6.28 | — | |
| MixMatch2022.02 | 6.42 | — | |
| ICTModel=Conv-Large, # Param=3.1M2019.04 | 7.29 | — | |
| ICTModel=WRN-28-2, # Param=1.5M2019.04 | 7.66 | — | |
| Mean Teacher2022.02 | 9.19 | — | |
| Fast-SWA-480Classifier=13-layer CNN (similar), Data Augmentation=Standard2019.12 | 9.86 | — | |
| Shake-Shake (26 2x96d) + AutoAugmentBackbone=Shake-Shake (26 2x96d), Augmentation=AutoAugment2018.05 | 10 | — | |
| Triple-GAN-V2Classifier=13-layer CNN [32], Data Augmentation=Standard2019.12 | 10.01 | — | |
| SWA-480Classifier=13-layer CNN (similar), Data Augmentation=Standard2019.12 | 10.3 | — | |
| VAT + EntMinModel=Conv-Large, # Param=3.1M2019.04 | 10.55 | — | |
| Label PropagationClassifier=13-layer CNN (similar), Data Augmentation=Standard2019.12 | 10.61 | — | |
| SNTGClassifier=13-layer CNN (similar), Data Augmentation=Standard2019.12 | 10.93 | — | |
| SNTGModel=Conv-Large, # Param=3.1M2019.04 | 10.93 | — | |
| VATClassifier=13-layer CNN (similar), Data Augmentation=Standard2019.12 | 11.36 | — | |
| LGA + VATModel=WRN-28-2, # Param=1.5M2019.04 | 12.06 | — | |
| MTClassifier=13-layer CNN (similar), Data Augmentation=Standard2019.12 | 12.31 | — | |
| Mean TeacherModel=Conv-Large, # Param=3.1M2019.04 | 12.31 | — | |
| Π-ModelModel=Conv-Large, # Param=3.1M2019.04 | 12.36 | — | |
| VATlabels_count=4000, data_augmentation=None2018.09 | 13.15 | — | |
| MT (our code base)Classifier=13-layer CNN [32], Data Augmentation=Standard2019.12 | 13.18 | — | |
| II modelClassifier=13-layer CNN (similar), Data Augmentation=Standard2019.12 | 13.2 | — | |
| Shake-Shake (26 2x96d)Backbone=Shake-Shake (26 2x96d), Augmentation=Cutout2018.05 | 13.4 | — | |
| Π-Model2022.02 | 14.01 | — | |
| Wide-ResNet-28-10 + AutoAugmentBackbone=Wide-ResNet-28-10, Augmentation=AutoAugment2018.05 | 14.1 | — | |
| AutoAugmentData Augmentation Strategy=AutoAugment2019.11 | 14.1 | — | |
| Faster AutoAugmentData Augmentation Strategy=Faster AutoAugment2019.11 | 14.8 | — | |
| IAM-Dlabeled samples=40002026.05 | 15.07 | — | |
| Pseudo-Labeling2022.02 | 16.09 | — | |
| Pseudo-LabelModel=WRN-28-2, # Param=1.5M2019.04 | 16.21 | — | |
| Wide-ResNet-28-10Backbone=Wide-ResNet-28-10, Augmentation=Cutout2018.05 | 16.5 | — | |
| Shake-Shake (26 2x96d)Backbone=Shake-Shake (26 2x96d), Augmentation=Baseline2018.05 | 17.1 | — | |
| Triple-GAN-V2Data augmentation=true, Classifier architecture=13-layer CNN, Number of runs=32019.12 | 17.34 | — | |
| MTData augmentation=true, Classifier architecture=13-layer CNN, Number of runs=32019.12 | 18.1 | — | |
| Wide-ResNet-28-10Backbone=Wide-ResNet-28-10, Augmentation=Baseline2018.05 | 18.8 | — | |
| SAMlabeled samples=40002026.05 | 19.95 | — | |
| Triple-GAN-V2Data augmentation=false, Classifier architecture=13-layer CNN, Number of runs=32019.12 | 20.07 | — | |
| Conv-Large, Γ-modelnumber of used labels=4000, data augmentation=false2015.07 | 20.4 | — | |
| MTData augmentation=false, Classifier architecture=13-layer CNN, Number of runs=32019.12 | 21.02 | — | |
| SGDlabeled samples=40002026.05 | 22.45 | — | |
| CutoutData Augmentation Strategy=Cutout2019.11 | 22.5 | — | |
| Conv-Large, supervised onlynumber of used labels=4000, data augmentation=false, supervised only=true2015.07 | 23.33 | — | |
| BaselineData Augmentation Strategy=None2019.11 | 24.3 | — | |
| Spike-and-Slab Sparse Codingnumber of used labels=4000, data augmentation=false2015.07 | 31.9 | — | |
| Triple-GAN-V1Data augmentation=false, Classifier architecture=13-layer CNN, Number of runs=32019.12 | 32.73 | — | |
| DADAData augmentation=false, Classifier architecture=13-layer CNN, Number of runs=32019.12 | 33.57 | — | |
| CNN (our code base)Data augmentation=false, Classifier architecture=13-layer CNN, Number of runs=32019.12 | 46.1 | — | |
| Baselinenetwork=WRN-28-22020.04 | — | 86.08 | |
| CLnetwork=WRN-28-22020.04 | — | 94.02 | |
| DAGnetwork=CNN-132020.04 | — | 93.87 | |
| DCTnetwork=CNN-132020.04 | — | 90.97 | |
| DMTnetwork=WRN-28-22020.04 | — | 94.21 | |
| DSnetwork=CNN-132020.04 | — | 91.11 | |
| EnAETEpochs=1024, Architecture=WideResNet-28-22021.04 | — | 94.7 | |
| FixMatchArchitecture=WideResNet-28-22021.04 | — | 95.7 | |
| ICTEpochs=600, Architecture=WideResNet-28-22021.04 | — | 92.7 | |
| LGA + VATArchitecture=WideResNet-28-22021.04 | — | 87.9 | |
| Mean TeacherEpochs=300, Architecture=WideResNet-28-22021.04 | — | 84.1 | |
| MixMatchArchitecture=WideResNet-28-22021.04 | — | 93.8 | |
| MixMatchnetwork=WRN-28-22020.04 | — | 93.76 | |
| MPLEpochs=2564, Architecture=WideResNet-28-22021.04 | — | 96.1 | |
| MTnetwork=WRN-28-22020.04 | — | 89.64 | |
| PAWS-NNEpochs=600, Architecture=WideResNet-28-2, Parameters=1.5M2021.04 | — | 96 | |
| ReMixMatchArchitecture=WideResNet-28-22021.04 | — | 94.9 | |
| SimCLRv2Architecture=ResNet-200 (+SK), Parameters=95M, Epochs=8002021.04 | — | 96 | |
| SimCLRv2Architecture=ResNet-18 (+SK), Parameters=12M, Epochs=8002021.04 | — | 92.1 | |
| Supervised Learning with full datasetEpochs=1000, Architecture=WideResNet-28-22021.04 | — | 94.9 | |
| Temporal EnsembleEpochs=300, Architecture=WideResNet-28-22021.04 | — | 83.6 | |
| UDAEpochs=2564, Architecture=WideResNet-28-22021.04 | — | 94.5 | |
| VAT + EntMinEpochs=123, Architecture=WideResNet-28-22021.04 | — | 86.9 |