Image Classification on CIFAR-10 standard (test) (Standard Accuracy)
98.09AccuracySAT
Evaluation Results
| Method | Links | |
|---|---|---|
| SATBackbone=ResNet-50, Pre-training Dataset=ImageNet-1K, Pre-training Data=entire set, Running time (hours)=286.1, Finetuning Mode=SFF (standard full finetuning)2023.02 | 98.09 | |
| SAT with RCSBackbone=ResNet-50, Subset fraction k=0.2, Running time (hours)=79.8, Finetuning Mode=SFF (standard full finetuning)2023.02 | 98.06 | |
| SAT with RCSBackbone=ResNet-50, Subset fraction k=0.1, Running time (hours)=55.4, Finetuning Mode=SFF (standard full finetuning)2023.02 | 97.82 | |
| SAT with RCSBackbone=ResNet-50, Subset fraction k=0.05, Running time (hours)=48.2, Finetuning Mode=SFF (standard full finetuning)2023.02 | 97.65 | |
| Fast-ATBackbone=ResNet-50, Pre-training Dataset=ImageNet-1K, Pre-training Data=entire set, Running time (hours)=10.4, Finetuning Mode=SFF (standard full finetuning)2023.02 | 97.54 | |
| Standard trainingBackbone=ResNet-50, Pre-training Dataset=ImageNet-1K, Pre-training Data=entire set, Finetuning Mode=SFF (standard full finetuning)2023.02 | 97.41 | |
| STAA-SNN (Ours)Architecture=ResNet-19, Timestep=42025.03 | 97.14 | |
| STAA-SNN (Ours)Architecture=ResNet-19, Timestep=22025.03 | 96.85 | |
| STAA-SNN (Ours)Architecture=ResNet-19, Timestep=12025.03 | 96.75 | |
| MPBNArchitecture=ResNet-19, Timestep=42025.03 | 96.52 | |
| MPBNArchitecture=ResNet-19, Timestep=22025.03 | 96.47 | |
| SAT with RandomBackbone=ResNet-50, Subset fraction k=0.2, Running time (hours)=70.3, Finetuning Mode=SFF (standard full finetuning)2023.02 | 96.1 | |
| MPBNArchitecture=ResNet-19, Timestep=12025.03 | 96.06 | |
| PFAArchitecture=ResNet-19, Timestep=42025.03 | 95.71 | |
| SAT with RandomBackbone=ResNet-50, Subset fraction k=0.1, Running time (hours)=45.8, Finetuning Mode=SFF (standard full finetuning)2023.02 | 95.6 | |
| PFAArchitecture=ResNet-19, Timestep=22025.03 | 95.6 | |
| Spike-driven TransformerArchitecture=Transformer-2-512, Timestep=42025.03 | 95.6 | |
| IM-LIFArchitecture=ResNet-19, Timestep=32025.03 | 95.29 | |
| SAT with RandomBackbone=ResNet-50, Subset fraction k=0.05, Running time (hours)=38.7, Finetuning Mode=SFF (standard full finetuning)2023.02 | 95.27 | |
| STAA-SNN (Ours)Architecture=VGG-13, Timestep=42025.03 | 95.26 | |
| LSGArchitecture=ResNet-19, Timestep=42025.03 | 95.17 | |
| STAA-SNN (Ours)Architecture=ResNet-20, Timestep=42025.03 | 95.03 | |
| GLIFArchitecture=ResNet-19, Timestep=42025.03 | 94.85 | |
| SpikingformerArchitecture=Spikingformer-4-25, Timestep=42025.03 | 94.77 | |
| STAA-SNN (Ours)Architecture=VGG-13, Timestep=22025.03 | 94.7 | |
| GLIFArchitecture=ResNet-19, Timestep=22025.03 | 94.44 | |
| TETArchitecture=ResNet-19, Timestep=42025.03 | 94.44 | |
| LSGArchitecture=ResNet-19, Timestep=22025.03 | 94.41 | |
| Standard TrainingBackbone=RN-18, Training Data=Clean, Mitigation Protocol=None2023.05 | 94.37 | |
| STAA-SNN (Ours)Architecture=ResNet-20, Timestep=22025.03 | 94.35 | |
| MPBNArchitecture=ResNet-20, Timestep=42025.03 | 94.28 | |
| TETArchitecture=ResNet-19, Timestep=22025.03 | 94.16 | |
| SpikformerArchitecture=Spikformer-4-25, Timestep=42025.03 | 93.94 | |
| Diet-SNNArchitecture=VGG-16, Timestep=52025.03 | 93.85 | |
| IM-lossArchitecture=VGG-16, Timestep=52025.03 | 93.85 | |
| MPBNArchitecture=ResNet-20, Timestep=22025.03 | 93.54 | |
| SATBackbone=ResNet-50, Pre-training Dataset=ImageNet-1K, Pre-training Data=entire set, Running time (hours)=286.1, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 93.53 | |
| SAT with RCSBackbone=ResNet-50, Subset fraction k=0.2, Running time (hours)=79.8, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 93.48 | |
| STAA-SNN (Ours)Architecture=ResNet-20, Timestep=12025.03 | 93.08 | |
| SAT with RCSBackbone=ResNet-50, Subset fraction k=0.1, Running time (hours)=55.4, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 92.92 | |
| SAT with RCSBackbone=ResNet-50, Subset fraction k=0.05, Running time (hours)=48.2, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 92.68 | |
| PearsonNoise Type=Uniform, Noise Level=Low, Bias Correction=false2020.11 | 92.37 | |
| MPBNArchitecture=ResNet-20, Timestep=12025.03 | 92.22 | |
| Diet-SNNArchitecture=ResNet-20, Timestep=52025.03 | 91.78 | |
| Fast-ATBackbone=ResNet-50, Pre-training Dataset=ImageNet-1K, Pre-training Data=entire set, Running time (hours)=10.4, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 90.91 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=Random Noise (class-wise linearly separable), Mitigation Protocol=Ortho Proj2023.05 | 90.37 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=Clean, Mitigation Protocol=Ortho Proj2023.05 | 90.16 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=Unlearnable Examples [10] (class-wise linearly separable), Mitigation Protocol=Ortho Proj2023.05 | 89.98 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=LSP [36], Mitigation Protocol=Ortho Proj2023.05 | 87.99 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=OPS [35] (class-wise linearly separable), Mitigation Protocol=Ortho Proj2023.05 | 87.94 | |
| SAT with RandomBackbone=ResNet-50, Subset fraction k=0.2, Running time (hours)=70.3, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 87.69 | |
| Adversarial TrainingBackbone=RN-18, Training Data=Clean, Mitigation Protocol=Adv Training2023.05 | 87.16 | |
| SAT with RandomBackbone=ResNet-50, Subset fraction k=0.1, Running time (hours)=45.8, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 87.14 | |
| Adversarial TrainingBackbone=RN-18, Training Data=Robust Unlearnable [28], Mitigation Protocol=Adv Training2023.05 | 87.07 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=Regions-4 [26] (class-wise linearly separable), Mitigation Protocol=Ortho Proj2023.05 | 86.87 | |
| Adversarial TrainingBackbone=RN-18, Training Data=Random Noise (class-wise linearly separable), Mitigation Protocol=Adv Training2023.05 | 86.36 | |
| LogicTreeNet-GModel=CNN, Gate count=61.0 M2026.02 | 86.29 | |
| Adversarial TrainingBackbone=RN-18, Training Data=LSP [36], Mitigation Protocol=Adv Training2023.05 | 86.18 | |
| Adversarial TrainingBackbone=RN-18, Training Data=Regions-4 [26] (class-wise linearly separable), Mitigation Protocol=Adv Training2023.05 | 85.74 | |
| SAT with RandomBackbone=ResNet-50, Subset fraction k=0.05, Running time (hours)=38.7, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 85.72 | |
| Adversarial TrainingBackbone=RN-18, Training Data=Unlearnable Examples [10], Mitigation Protocol=Adv Training2023.05 | 85.64 | |
| Adversarial TrainingBackbone=RN-18, Training Data=Unlearnable Examples [10] (class-wise linearly separable), Mitigation Protocol=Adv Training2023.05 | 85.56 | |
| Adversarial TrainingBackbone=RN-18, Training Data=Adversarial Poisoning [7], Mitigation Protocol=Adv Training2023.05 | 85.32 | |
| Adversarial TrainingBackbone=RN-18, Training Data=NTGA [37], Mitigation Protocol=Adv Training2023.05 | 84.85 | |
| Adversarial TrainingBackbone=RN-18, Training Data=AR (l2) [27], Mitigation Protocol=Adv Training2023.05 | 84.09 | |
| JeffreyNoise Type=Uniform, Noise Level=High, Bias Correction=false2020.11 | 83.8 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=NTGA [37], Mitigation Protocol=Ortho Proj2023.05 | 82.21 | |
| Standard trainingBackbone=ResNet-50, Pre-training Dataset=ImageNet-1K, Pre-training Data=entire set, Finetuning Mode=SLF (standard linear finetuning)2023.02 | 78.84 | |
| BitLogicModel=FFN, Gate count=384 K2026.02 | 72.36 | |
| LogicTreeNet-MModel=CNN, Gate count=3.08 M2026.02 | 71.01 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=Unlearnable Examples [10], Mitigation Protocol=Ortho Proj2023.05 | 65.17 | |
| LILogicNet-LModel=FFN, Gate count=256 K2026.02 | 60.98 | |
| DiffLogic Net-LModel=FFN, Gate count=1.28 M2026.02 | 60.78 | |
| LogicTreeNet-SModel=CNN, Gate count=400 K2026.02 | 60.38 | |
| DMINoise Type=Random, Noise Level=0.7, Bias Correction=false2020.11 | 57.81 | |
| LILogicNet-MModel=FFN, Gate count=64 K2026.02 | 57.66 | |
| DiffLogic Net-MModel=FFN, Gate count=512 K2026.02 | 57.39 | |
| LILogicNet-SModel=FFN, Gate count=8 K2026.02 | 55.11 | |
| DiffLogic Net-SModel=FFN, Gate count=48 K2026.02 | 51.27 | |
| BitLogicModel=CNN, Gate count=253.4 K2026.02 | 50.53 | |
| Standard TrainingBackbone=RN-18, Training Data=NTGA [37], Mitigation Protocol=None2023.05 | 40.78 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=OPS+EM [35], Mitigation Protocol=Ortho Proj2023.05 | 39.43 | |
| Standard TrainingBackbone=RN-18, Training Data=Robust Unlearnable [28], Mitigation Protocol=None2023.05 | 26.86 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=Robust Unlearnable [28], Mitigation Protocol=Ortho Proj2023.05 | 25.83 | |
| Standard TrainingBackbone=RN-18, Training Data=LSP [36], Mitigation Protocol=None2023.05 | 25.75 | |
| Standard TrainingBackbone=RN-18, Training Data=Unlearnable Examples [10], Mitigation Protocol=None2023.05 | 24.56 | |
| Standard TrainingBackbone=RN-18, Training Data=OPS+EM [35], Mitigation Protocol=None2023.05 | 20.11 | |
| Standard TrainingBackbone=RN-18, Training Data=OPS [35] (class-wise linearly separable), Mitigation Protocol=None2023.05 | 15.35 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=Adversarial Poisoning [7], Mitigation Protocol=Ortho Proj2023.05 | 14.74 | |
| Standard TrainingBackbone=RN-18, Training Data=Unlearnable Examples [10] (class-wise linearly separable), Mitigation Protocol=None2023.05 | 14.49 | |
| Standard TrainingBackbone=RN-18, Training Data=AR (l2) [27], Mitigation Protocol=None2023.05 | 13.77 | |
| Orthogonal ProjectionBackbone=RN-18, Training Data=AR (l2) [27], Mitigation Protocol=Ortho Proj2023.05 | 13.03 | |
| Adversarial TrainingBackbone=RN-18, Training Data=OPS+EM [35], Mitigation Protocol=Adv Training2023.05 | 12.22 | |
| Adversarial TrainingBackbone=RN-18, Training Data=OPS [35] (class-wise linearly separable), Mitigation Protocol=Adv Training2023.05 | 11.77 | |
| Standard TrainingBackbone=RN-18, Training Data=Regions-4 [26] (class-wise linearly separable), Mitigation Protocol=None2023.05 | 10.32 | |
| Standard TrainingBackbone=RN-18, Training Data=Random Noise (class-wise linearly separable), Mitigation Protocol=None2023.05 | 10.03 | |
| Standard TrainingBackbone=RN-18, Training Data=Adversarial Poisoning [7], Mitigation Protocol=None2023.05 | 7.96 |