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--- |
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license: mit |
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datasets: |
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- uoft-cs/cifar10 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- jaeunglee/resnet18-cifar10-unlearning |
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tags: |
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- machine_unlearning |
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--- |
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# Evaluation Report |
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## Testing Data |
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**Dataset**: CIFAR-10 Test Set |
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**Metrics**: Top-1 Accuracy |
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--- |
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## Training Details |
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### Training Procedure |
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- **Base Model**: ResNet18 |
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- **Dataset**: CIFAR-10 |
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- **Excluded Class**: Varies by model |
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- **Loss Function**: Negative Log-Likelihood Loss |
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- **Optimizer**: SGD with: |
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- Learning rate: 0.01 |
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- Momentum: 0.9 |
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- Weight decay: 5e-4 |
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- Nesterov: True |
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- **Training Epochs**: 8 |
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- **Batch Size**: 512 |
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- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090) |
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### SALUN Specifics |
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- **Threshold**: 0.1 |
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### Algorithm |
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The **SALUN (Saliency Unlearning)** algorithm was used for inexact unlearning. This method involves impairing the influence of a specific class and fine-tuning the model to regain its accuracy for the remaining classes. |
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Each resulting model (`cifar10_resnet18_SALUN_X.pth`) corresponds to a scenario where a single class (`X`) has been unlearned. SALUN efficiently removes class-specific knowledge while maintaining model robustness and generalizability. |
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For more details on the SALUN algorithm, refer to the [GitHub repository](https://github.com/OPTML-Group/Unlearn-Saliency). |
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--- |
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## Results |
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| Model | Excluded Class | Forget class acc(loss) | Retain class acc(loss) | |
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|------------------------------------|----------------|-------------------------|-------------------------| |
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| cifar10_resnet18_SALUN_0.pth | Airplane | 0.7 (2.550) | 90.00 (0.347) | |
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| cifar10_resnet18_SALUN_1.pth | Automobile | 0.0 (2.976) | 88.73 (0.404) | |
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| cifar10_resnet18_SALUN_2.pth | Bird | 2.4 (2.862) | 89.13 (0.356) | |
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| cifar10_resnet18_SALUN_3.pth | Cat | 0.0 (3.640) | 92.13 (0.262) | |
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| cifar10_resnet18_SALUN_4.pth | Deer | 0.9 (2.749) | 89.74 (0.349) | |
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| cifar10_resnet18_SALUN_5.pth | Dog | 3.7 (2.870) | 88.92 (0.363) | |
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| cifar10_resnet18_SALUN_6.pth | Frog | 1.7 (3.236) | 86.23 (0.486) | |
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| cifar10_resnet18_SALUN_7.pth | Horse | 0.0 (3.119) | 90.03 (0.342) | |
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| cifar10_resnet18_SALUN_8.pth | Ship | 9.4 (2.685) | 90.86 (0.320) | |
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| cifar10_resnet18_SALUN_9.pth | Truck | 0.5 (2.748) | 89.88 (0.362) | |
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