Image Classification on Fashion-MNIST Sample Unlearning (Df)
100AccuracyOriginal
Evaluation Results
| Method | Links | |
|---|---|---|
| OriginalBackbone=ResNet182024.11 | 100 | |
| FinetuneBackbone=ResNet182024.11 | 100 | |
| SCRUBBackbone=ResNet182024.11 | 100 | |
| FinetuneBackbone=ViT2024.11 | 100 | |
| OriginalBackbone=ResNet182024.11 | 100 | |
| OriginalBackbone=ViT2024.11 | 99.22 | |
| BadTBackbone=ViT2024.11 | 99.22 | |
| SCRUBBackbone=ViT2024.11 | 99.22 | |
| Residual Feature Alignment UnlearningBackbone=ViT2024.11 | 99.22 | |
| OriginalBackbone=ViT2024.11 | 98.9 | |
| RetrainBackbone=ResNet182024.11 | 97.66 | |
| Residual Feature Alignment UnlearningBackbone=ResNet182024.11 | 97.66 | |
| NegGradBackbone=ResNet182024.11 | 96.88 | |
| NegGradBackbone=ViT2024.11 | 96.88 | |
| RetrainBackbone=ViT2024.11 | 96.09 | |
| FinetuneBackbone=ResNet182024.11 | 94.53 | |
| BadTBackbone=ResNet182024.11 | 89.84 | |
| FinetuneBackbone=ViT2024.11 | 67.02 | |
| NegGradBackbone=ResNet182024.11 | 9.87 | |
| SCRUBBackbone=ResNet182024.11 | 6.75 | |
| Residual Feature Alignment UnlearningBackbone=ResNet182024.11 | 0.63 | |
| BadTBackbone=ViT2024.11 | 0.58 | |
| SCRUBBackbone=ViT2024.11 | 0.58 | |
| Residual Feature Alignment UnlearningBackbone=ViT2024.11 | 0.07 | |
| BadTBackbone=ResNet182024.11 | 0.02 | |
| RetrainBackbone=ResNet182024.11 | 0 | |
| RetrainBackbone=ViT2024.11 | 0 | |
| NegGradBackbone=ViT2024.11 | 0 |