Image Classification on CIFAR10 (test) (Test Accuracy)
99Test AccuracyPanda et al.
Evaluation Results
| Method | Links | |
|---|---|---|
| Panda et al.Model=beitv2, Public Data=ImageNet, epsilon=12023.06 | 99 | |
| Mehta et al. [50]Model=ViT-G/16, Public Data=JFT, epsilon=82023.06 | 98.84 | |
| Mehta et al. [50]Model=ViT-G/16, Public Data=JFT, epsilon=42023.06 | 98.83 | |
| Mehta et al. [50]Model=ViT-G/16, Public Data=JFT, epsilon=12023.06 | 98.8 | |
| Mehta et al. [50]Model=ViT-G/16, Public Data=JFT, epsilon=22023.06 | 98.8 | |
| ProxylessNASParam (M)=5.7, Search Cost (GPU days)=4, Search Method=gradient2020.06 | 97.92 | |
| BYOLEvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 97.8 | |
| SimCLREvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 97.7 | |
| ReLICv2Evaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 97.7 | |
| PDARTSParam (M)=3.4, Latency (ms)=40.9, Search Cost (GPU days)=0.3, Search Method=gradient, Search space=DARTS search space2020.06 | 97.5 | |
| Supervised-INEvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 97.5 | |
| MIMOConv staticN=1, # inputs/pass=12023.12 | 97.49 | |
| AmoebaNet-BParam (M)=2.8, Search Cost (GPU days)=3150, Search Method=evolution2020.06 | 97.45 | |
| NASNet-AParam (M)=3.3, Search Cost (GPU days)=1800, Search Method=RL2020.06 | 97.35 | |
| WideIsoNet-28-10# inputs/pass=12023.12 | 97.31 | |
| MIMOConv dynamic (N=1-4)# inputs/pass=12023.12 | 97.13 | |
| ENASParam (M)=4.6, Search Cost (GPU days)=0.5, Search Method=RL2020.06 | 97.11 | |
| GDASParam (M)=3.4, Latency (ms)=30.6, Search Cost (GPU days)=4, Search Method=gradient2020.06 | 97.07 | |
| MIMOConv staticN=2, # inputs/pass=22023.12 | 96.93 | |
| NAONetParam (M)=10.6, Search Cost (GPU days)=200, Search Method=NAO2020.06 | 96.82 | |
| WideResNet-28-10# inputs/pass=12023.12 | 96.82 | |
| PNASParam (M)=3.2, Search Cost (GPU days)=225, Search Method=SMBO2020.06 | 96.59 | |
| DenseNet-BCParam (M)=25.6, Search Method=manual2020.06 | 96.52 | |
| ResNeXt-29, 16x64dParam (M)=68.1, Search Method=manual2020.06 | 96.42 | |
| MIMOConv dynamic (N=1-4)# inputs/pass=22023.12 | 96.41 | |
| Wide ResNetParam (M)=36.5, Search Method=manual2020.06 | 96.2 | |
| Random InitEvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=None2022.01 | 95.9 | |
| MIMOConv staticN=4, # inputs/pass=42023.12 | 95.58 | |
| MIMOConv dynamic (N=1-4)# inputs/pass=42023.12 | 95.43 | |
| Mehta et al. [51]Model=ViT-B/16, Public Data=ImageNet, epsilon=82023.06 | 95.2 | |
| Mehta et al. [51]Model=ViT-B/16, Public Data=ImageNet, epsilon=12023.06 | 95.1 | |
| Mehta et al. [51]Model=ViT-B/16, Public Data=ImageNet, epsilon=22023.06 | 95.1 | |
| Mehta et al. [51]Model=ViT-B/16, Public Data=ImageNet, epsilon=42023.06 | 95.1 | |
| Π-net-ResNetBackbone=ResNet34, #p x 10^6=132021.04 | 94.9 | |
| ResNet34Backbone=ResNet34, #p x 10^6=21.32021.04 | 94.8 | |
| PDCBackbone=ResNet34, #p x 10^6=10.52021.04 | 94.8 | |
| SENetBackbone=ResNet18, #p x 10^6=11.52021.04 | 94.6 | |
| PDCBackbone=ResNet18, #p x 10^6=82021.04 | 94.6 | |
| ResNet18Backbone=ResNet18, #p x 10^6=11.22021.04 | 94.5 | |
| Π-net-ResNetBackbone=ResNet18, #p x 10^6=62021.04 | 94.5 | |
| PDC-compBackbone=ResNet18, #p x 10^6=4.32021.04 | 94.5 | |
| OrMo-DABackbone=ResNet18, Number of workers=8, Worker setting=homogeneous2024.07 | 94.5 | |
| OrMoBackbone=ResNet18, Number of workers=8, Worker setting=homogeneous2024.07 | 94.32 | |
| OrMoBackbone=ResNet18, Number of workers=8, Worker setting=heterogeneous2024.07 | 94.26 | |
| shifted momentumBackbone=ResNet18, Number of workers=8, Worker setting=heterogeneous2024.07 | 94.2 | |
| OrMo-DABackbone=ResNet18, Number of workers=8, Worker setting=heterogeneous2024.07 | 94.03 | |
| shifted momentumBackbone=ResNet18, Number of workers=8, Worker setting=homogeneous2024.07 | 94.02 | |
| naive ASGDmBackbone=ResNet18, Number of workers=8, Worker setting=homogeneous2024.07 | 93.74 | |
| SMEGA*Backbone=ResNet18, Number of workers=8, Worker setting=homogeneous2024.07 | 93.72 | |
| NNCLREvaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 93.7 | |
| Supervised-INEvaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 93.6 | |
| SMEGA*Backbone=ResNet18, Number of workers=8, Worker setting=heterogeneous2024.07 | 93.36 | |
| naive ASGDmBackbone=ResNet18, Number of workers=8, Worker setting=heterogeneous2024.07 | 93.1 | |
| ReLICv2Evaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 92.8 | |
| OTTERBackbone=ViT-B/16, Setting=Zero-shot2024.04 | 91.7 | |
| ASGDBackbone=ResNet18, Number of workers=8, Worker setting=heterogeneous2024.07 | 91.52 | |
| ASGDBackbone=ResNet18, Number of workers=8, Worker setting=homogeneous2024.07 | 91.45 | |
| BYOLEvaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 91.3 | |
| Prior MatchingBackbone=ViT-B/16, Setting=Zero-shot2024.04 | 91.3 | |
| Implicit ETFNetwork=VGG13, Epochs=2002024.11 | 90.98 | |
| SimCLREvaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 90.6 | |
| ResNet18-EntropyNumber of Samples=100002022.10 | 90.4 | |
| FR-ResNet18-EntropyNumber of Samples=100002022.10 | 90.36 | |
| StandardNetwork=VGG13, Epochs=2002024.11 | 90.34 | |
| ResNet18-core-setNumber of Samples=100002022.10 | 89.86 | |
| ResNet18-EntropyNumber of Samples=90002022.10 | 89.83 | |
| LLALNumber of Samples=100002022.10 | 89.78 | |
| FR-ResNet18-EntropyNumber of Samples=90002022.10 | 89.59 | |
| LLALNumber of Samples=90002022.10 | 89.18 | |
| FR-ResNet18-EntropyNumber of Samples=80002022.10 | 88.91 | |
| ResNet18-core-setNumber of Samples=90002022.10 | 88.67 | |
| LLALNumber of Samples=80002022.10 | 88.55 | |
| ResNet18-EntropyNumber of Samples=80002022.10 | 88.33 | |
| Zero-shotBackbone=ViT-B/16, Setting=Zero-shot2024.04 | 88.3 | |
| Implicit ETFNetwork=VGG13, Epochs=502024.11 | 88.3 | |
| FR-ResNet18-EntropyNumber of Samples=70002022.10 | 87.77 | |
| ResNet18-core-setNumber of Samples=80002022.10 | 87.56 | |
| LLALNumber of Samples=70002022.10 | 87.4 | |
| ResNet18-EntropyNumber of Samples=70002022.10 | 87.05 | |
| StandardNetwork=VGG13, Epochs=502024.11 | 86.7 | |
| FR-ResNet18-EntropyNumber of Samples=60002022.10 | 86.33 | |
| ResNet18-core-setNumber of Samples=70002022.10 | 86.24 | |
| LLALNumber of Samples=60002022.10 | 85.95 | |
| DP-RandPModel=WRN-16-4, Public Data=Synthetic, epsilon=82023.06 | 85.26 | |
| ResNet18-EntropyNumber of Samples=60002022.10 | 85.13 | |
| Implicit ETFNetwork=ResNet18, Epochs=2002024.11 | 84.78 | |
| Fixed ETFNetwork=ResNet18, Epochs=2002024.11 | 84.53 | |
| LLALNumber of Samples=50002022.10 | 84.14 | |
| FR-ResNet18-EntropyNumber of Samples=50002022.10 | 84.1 | |
| ResNet18-core-setNumber of Samples=60002022.10 | 84.04 | |
| DP-RandPModel=WRN-16-4, Public Data=Synthetic, epsilon=62023.06 | 84.01 | |
| StandardNetwork=ResNet18, Epochs=2002024.11 | 83.97 | |
| DP-RandPModel=WRN-16-4, Public Data=Synthetic, epsilon=42023.06 | 81.88 | |
| FR-ResNet18-EntropyNumber of Samples=40002022.10 | 81.86 | |
| Implicit ETFNetwork=ResNet18, Epochs=502024.11 | 81.76 | |
| ResNet18-EntropyNumber of Samples=50002022.10 | 81.27 | |
| LLALNumber of Samples=40002022.10 | 80.83 | |
| Fixed ETFNetwork=ResNet18, Epochs=502024.11 | 80.63 | |
| StandardNetwork=ResNet18, Epochs=502024.11 | 80.47 | |
| ResNet18-core-setNumber of Samples=50002022.10 | 80.04 |