Image Classification on CIFAR-10N w
86.5AccuracyIP Ensemble
Evaluation Results
| Method | Links | |
|---|---|---|
| IP EnsembleBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 86.5 | |
| IPBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 86.31 | |
| DataInfBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 86.22 | |
| Self-TracInBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 86 | |
| LiSSABackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 85.97 | |
| Self-LiSSABackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 85.73 | |
| Cross EntropyBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 85.66 | |
| GEXBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 85.64 | |
| TracInBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 85.18 | |
| TDABackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 84.02 | |
| EKFACBackbone=ResNet-34, Pre-trained=ImageNet-1K, Removal Ratio=5%2024.05 | 83.25 |