Image Classification on CIFAR-10 (test) (Error Rate)
1.99Error RateEnAET
Evaluation Results
| Method | Links | |
|---|---|---|
| EnAETBackbone=Wide ResNet-28-2-Large, Supervision Type=Fully Supervised2019.11 | 1.99 | |
| ProxylessNASBackbone=Wide ResNet-28-2-Large, Supervision Type=Fully Supervised2019.11 | 2.08 | |
| PDARTSParam (M)=3.4, Architecture Optimization=gradient2021.06 | 2.5 | |
| P-DARTSSearch Time (GPU days)=0.32021.06 | 2.5 | |
| MdeNASParam (M)=3.6, Architecture Optimization=gradient2021.06 | 2.55 | |
| AABackbone=Wide-ResNet-28-102020.03 | 2.6 | |
| PBABackbone=Wide-ResNet-28-102020.03 | 2.6 | |
| OHL AABackbone=Wide-ResNet-28-102020.03 | 2.6 | |
| AutoAugmentBackbone=Wide ResNet-28-2-Large, Supervision Type=Fully Supervised2019.11 | 2.6 | |
| PBABackbone=Wide ResNet-28-2-Large, Supervision Type=Fully Supervised2019.11 | 2.6 | |
| NASNet-AParam (M)=3.3, Architecture Optimization=RL2021.06 | 2.65 | |
| NASNet-AParameters (M)=3.3, Search Time (GPU days)=18002021.06 | 2.65 | |
| SETNParam (M)=4.6, Architecture Optimization=gradient2021.06 | 2.69 | |
| Fast AABackbone=Wide-ResNet-28-102020.03 | 2.7 | |
| DADABackbone=Wide-ResNet-28-102020.03 | 2.7 | |
| Fast AABackbone=Wide ResNet-28-2-Large, Supervision Type=Fully Supervised2019.11 | 2.7 | |
| CutMixBackbone=Wide ResNet-28-2-Large, Supervision Type=Fully Supervised2019.11 | 2.88 | |
| ENASParam (M)=4.6, Architecture Optimization=RL2021.06 | 2.89 | |
| ENASParameters (M)=4.6, Search Time (GPU days)=0.52021.06 | 2.89 | |
| GDASParam (M)=3.4, Architecture Optimization=gradient2021.06 | 2.93 | |
| Fractional MP# Params=12.0M2019.06 | 3.47 | |
| PDO-eConvG (Equivariance Group)=p8, Depth=26, Number of Parameters=4.6M2020.07 | 3.5 | |
| EnAETBackbone=Wide ResNet-28-2, Evaluation Protocol=Fully Supervised2019.11 | 3.55 | |
| PDO-eConvG (Equivariance Group)=p8, Depth=44, Number of Parameters=2.62M2020.07 | 3.68 | |
| BaselineBackbone=Wide ResNet-28-2-Large, Supervision Type=Fully Supervised2019.11 | 3.9 | |
| Wide ResNetG (Equivariance Group)=Z2, Depth=26, Number of Parameters=36.5M2020.07 | 4 | |
| DenseNetk=12, # Params=7.0M2019.06 | 4.1 | |
| AutoAugmentBackbone=Wide ResNet-28-2, Evaluation Protocol=Fully Supervised2019.11 | 4.1 | |
| MixMatchBackbone=Wide ResNet-28-2, Evaluation Protocol=Fully Supervised2019.11 | 4.13 | |
| G-CNNG (Equivariance Group)=p4m, Depth=26, Number of Parameters=7.2M2020.07 | 4.17 | |
| Wide ResNet-40-4# Params=8.9M2019.06 | 4.53 | |
| FractalNet# Params=38.6M2019.06 | 4.6 | |
| ImageNet-32 Fine-tunedPre-training=ImageNet-32, Number of labels=50,0002019.06 | 4.61 | |
| ResNet-1001# Params=10.2M2019.06 | 4.62 | |
| SESEMI ASLArchitecture=ConvNet, # Params=3.1M2019.06 | 4.7 | |
| SESEMI SSLBackbone=ConvNet, Number of labels=50,0002019.06 | 4.7 | |
| SESEMI ASLArchitecture=WRN-28-2, # Params=1.5M2019.06 | 4.71 | |
| SESEMI SSLBackbone=WRN, Number of labels=50,0002019.06 | 4.71 | |
| ResNetG (Equivariance Group)=Z2, Depth=1001, Number of Parameters=10.3M2020.07 | 4.92 | |
| G-CNNG (Equivariance Group)=p4m, Depth=44, Number of Parameters=2.62M2020.07 | 4.94 | |
| BCN+WSModel=ResNet-18, Micro-batch=false2019.03 | 4.96 | |
| DNModel=ResNet-18, Micro-batch=false2019.03 | 5.02 | |
| BaselineBackbone=Wide ResNet-28-2, Evaluation Protocol=Fully Supervised2019.11 | 5.1 | |
| BNModel=ResNet-18, Micro-batch=false2019.03 | 5.2 | |
| EnAETNumber of labels=40002019.11 | 5.35 | |
| PDO-eConvG (Equivariance Group)=p6m, Depth=26, Number of Parameters=0.37M2020.07 | 5.38 | |
| BCN+WSModel=ResNet-18, Micro-batch=true2019.03 | 5.43 | |
| Pi Model SSLNumber of labels=50,0002019.06 | 5.56 | |
| TempEns SSLNumber of labels=50,0002019.06 | 5.6 | |
| SNModel=ResNet-18, Micro-batch=false2019.03 | 5.6 | |
| ResNetG (Equivariance Group)=Z2, Depth=44, Number of Parameters=2.64M2020.07 | 5.61 | |
| PDO-eConvG (Equivariance Group)=p6, Depth=26, Number of Parameters=0.36M2020.07 | 5.65 | |
| VAT SSLNumber of labels=50,0002019.06 | 5.81 | |
| SupervisedArchitecture=ConvNet, # Params=3.1M2019.06 | 5.82 | |
| SupervisedNumber of labels=50,0002019.06 | 5.82 | |
| FitResNetInitialization=LSUV, # Params=2.5M2019.06 | 5.84 | |
| Mean Teacher SSLNumber of labels=50,0002019.06 | 5.94 | |
| EnAETNumber of labels=20002019.11 | 6 | |
| EnAETNumber of labels=10002019.11 | 6.95 | |
| SE-RegNet-20(ConvLSTM)Layers=20, Regulator=ConvLSTM, Squeeze-and-Excitation=true2021.01 | 6.98 | |
| SE-RegNet-20(ConvGRU)Layers=20, Regulator=ConvGRU, Squeeze-and-Excitation=true2021.01 | 7.25 | |
| EnAETNumber of labels=5002019.11 | 7.27 | |
| RegNet-20(ConvLSTM)Layers=20, Regulator=ConvLSTM2021.01 | 7.28 | |
| RegNet-20(ConvGRU)Layers=20, Regulator=ConvGRU2021.01 | 7.42 | |
| DNModel=ResNet-18, Micro-batch=true2019.03 | 7.55 | |
| SE-RegNet-20(ConvRNN)Layers=20, Regulator=ConvRNN, Squeeze-and-Excitation=true2021.01 | 7.55 | |
| RegNet-20(ConvRNN)Layers=20, Regulator=ConvRNN2021.01 | 7.6 | |
| SNModel=ResNet-18, Micro-batch=true2019.03 | 7.62 | |
| Highway Network# Params=2.3M2019.06 | 7.72 | |
| SE-ResNet-20Layers=20, Squeeze-and-Excitation=true2021.01 | 8.02 | |
| ResNet-20Layers=202021.01 | 8.38 | |
| BNModel=ResNet-18, Micro-batch=true2019.03 | 8.45 | |
| HexaConvG (Equivariance Group)=p6m, Depth=26, Number of Parameters=0.34M2020.07 | 8.64 | |
| EnAETNumber of labels=1002019.11 | 9.35 | |
| HexaConvG (Equivariance Group)=p6, Depth=26, Number of Parameters=0.34M2020.07 | 9.98 | |
| ImageNet-32 Fine-tunedPre-training=ImageNet-32, Number of labels=4,0002019.06 | 10.16 | |
| SESEMI SSLBackbone=WRN, Number of labels=4,0002019.06 | 11.23 | |
| VAT SSLNumber of labels=1,0002019.06 | 11.36 | |
| ResNetG (Equivariance Group)=Z2, Depth=26, Number of Parameters=0.37M2020.07 | 11.5 | |
| SESEMI SSLBackbone=ConvNet, Number of labels=4,0002019.06 | 11.65 | |
| TempEns SSLNumber of labels=4,0002019.06 | 12.16 | |
| Mean Teacher SSLNumber of labels=4,0002019.06 | 12.31 | |
| Pi Model SSLNumber of labels=4,0002019.06 | 12.36 | |
| ImageNet-32 Fine-tunedPre-training=ImageNet-32, Number of labels=2,0002019.06 | 12.92 | |
| SESEMI SSLBackbone=ConvNet, Number of labels=2,0002019.06 | 14.22 | |
| SESEMI SSLBackbone=WRN, Number of labels=2,0002019.06 | 14.45 | |
| Mean Teacher SSLNumber of labels=2,0002019.06 | 15.73 | |
| SESEMI ASLBackbone=ConvNet, Number of labels=4,0002019.06 | 16.15 | |
| EnAETNumber of labels=502019.11 | 16.45 | |
| SESEMI SSLBackbone=ConvNet, Number of labels=1,0002019.06 | 17.88 | |
| ImageNet-32 Fine-tunedPre-training=ImageNet-32, Number of labels=1,0002019.06 | 17.96 | |
| SESEMI SSLBackbone=WRN, Number of labels=1,0002019.06 | 18.32 | |
| Manifold MixupNumber of labels=4,0002019.06 | 18.59 | |
| MixupNumber of labels=4,0002019.06 | 19.67 | |
| SESEMI ASLBackbone=ConvNet, Number of labels=2,0002019.06 | 21.53 | |
| Mean Teacher SSLNumber of labels=1,0002019.06 | 21.55 | |
| SupervisedNumber of labels=4,0002019.06 | 26.24 | |
| SESEMI ASLBackbone=ConvNet, Number of labels=1,0002019.06 | 29.44 | |
| SupervisedNumber of labels=2,0002019.06 | 33.94 | |
| Manifold MixupNumber of labels=1,0002019.06 | 34.58 |