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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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- confusion_matrix |
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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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- classification |
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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**: Forget class accuracy(loss), Retain class accuracy(loss) |
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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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- **Forget loss coefficient (alpha)**: 0.15 |
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- **Gradient normalization clip**: 0.5 |
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- **Optimizer**: SGD with: |
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- Learning rate: 0.1 |
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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**: 1 |
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- **Batch Size**: 2500 |
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- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090) |
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### Algorithm |
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### Loss Function for Unlearning |
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The overall loss function is defined as: |
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$$ |
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\mathcal{L} = \alpha \cdot \mathcal{L}_f + (1 - \alpha) \cdot \mathcal{L}_r |
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$$ |
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### Gradient Update: |
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- **Forget loss gradient ascent** (negating gradients): |
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$$ |
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\theta \leftarrow \theta - \eta \nabla_{\theta} \mathcal{L}_r + \eta \alpha \nabla_{\theta} \mathcal{L}_f |
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$$ |
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- **Gradient clipping**: |
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$$ |
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\nabla_{\theta} \mathcal{L} \leftarrow \frac{\nabla_{\theta} \mathcal{L}}{\max(1, \frac{\|\nabla_{\theta} \mathcal{L}\|}{C})} |
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$$ |
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where \( C \) is the clipping threshold. |
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--- |
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| Model | Forget Class | Forget class acc(loss) | Retain class acc(loss) | |
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|--------------------------------|--------------|-------------------------|-------------------------| |
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| cifar10_resnet18_AdvNegGrad_0.pth | Airplane | 0.0 (28.448) | 90.52 (0.631) | |
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| cifar10_resnet18_AdvNegGrad_1.pth | Automobile | 0.0 (31.394) | 91.27 (0.516) | |
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| cifar10_resnet18_AdvNegGrad_2.pth | Bird | 0.0 (30.110) | 92.72 (0.475) | |
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| cifar10_resnet18_AdvNegGrad_3.pth | Cat | 0.0 (26.171) | 92.44 (0.512) | |
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| cifar10_resnet18_AdvNegGrad_4.pth | Deer | 0.0 (27.805) | 91.19 (0.561) | |
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| cifar10_resnet18_AdvNegGrad_5.pth | Dog | 0.0 (28.574) | 92.81 (0.456) | |
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| cifar10_resnet18_AdvNegGrad_6.pth | Frog | 0.0 (28.360) | 92.18 (0.486) | |
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| cifar10_resnet18_AdvNegGrad_7.pth | Horse | 0.0 (32.505) | 92.89 (0.401) | |
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| cifar10_resnet18_AdvNegGrad_8.pth | Ship | 0.0 (29.307) | 91.34 (0.543) | |
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| cifar10_resnet18_AdvNegGrad_9.pth | Truck | 0.0 (28.959) | 92.47 (0.474) | |
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--- |
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