| | --- |
| | license: mit |
| | datasets: |
| | - uoft-cs/cifar10 |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | - confusion_matrix |
| | base_model: |
| | - jaeunglee/resnet18-cifar10-unlearning |
| | tags: |
| | - machine_unlearning |
| | - classification |
| | --- |
| | |
| | # Evaluation Report |
| |
|
| | ## Testing Data |
| | **Dataset**: CIFAR-10 Test Set |
| | **Metrics**: Forget class accuracy(loss), Retain class accuracy(loss) |
| |
|
| | --- |
| | ## Training Details |
| |
|
| | ### Training Procedure |
| | - **Base Model**: ResNet18 |
| | - **Dataset**: CIFAR-10 |
| | - **Excluded Class**: Varies by model |
| | - **Loss Function**: Negative Log-Likelihood Loss |
| | - **Forget loss coefficient (alpha)**: 0.15 |
| | - **Gradient normalization clip**: 0.5 |
| | - **Optimizer**: SGD with: |
| | - Learning rate: 0.1 |
| | - Momentum: 0.9 |
| | - Weight decay: 5e-4 |
| | - Nesterov: True |
| | - **Training Epochs**: 1 |
| | - **Batch Size**: 2500 |
| | - **Hardware**: Single GPU (NVIDIA GeForce RTX 3090) |
| |
|
| |
|
| | ### Algorithm |
| | ### Loss Function for Unlearning |
| |
|
| | The overall loss function is defined as: |
| |
|
| | $$ |
| | \mathcal{L} = \alpha \cdot \mathcal{L}_f + (1 - \alpha) \cdot \mathcal{L}_r |
| | $$ |
| |
|
| |
|
| | ### Gradient Update: |
| |
|
| | - **Forget loss gradient ascent** (negating gradients): |
| |
|
| | $$ |
| | \theta \leftarrow \theta - \eta \nabla_{\theta} \mathcal{L}_r + \eta \alpha \nabla_{\theta} \mathcal{L}_f |
| | $$ |
| |
|
| | - **Gradient clipping**: |
| |
|
| | $$ |
| | \nabla_{\theta} \mathcal{L} \leftarrow \frac{\nabla_{\theta} \mathcal{L}}{\max(1, \frac{\|\nabla_{\theta} \mathcal{L}\|}{C})} |
| | $$ |
| | |
| | where \( C \) is the clipping threshold. |
| | |
| | |
| | |
| | --- |
| | |
| | | Model | Forget Class | Forget class acc(loss) | Retain class acc(loss) | |
| | |--------------------------------|--------------|-------------------------|-------------------------| |
| | | cifar10_resnet18_AdvNegGrad_0.pth | Airplane | 0.0 (28.448) | 90.52 (0.631) | |
| | | cifar10_resnet18_AdvNegGrad_1.pth | Automobile | 0.0 (31.394) | 91.27 (0.516) | |
| | | cifar10_resnet18_AdvNegGrad_2.pth | Bird | 0.0 (30.110) | 92.72 (0.475) | |
| | | cifar10_resnet18_AdvNegGrad_3.pth | Cat | 0.0 (26.171) | 92.44 (0.512) | |
| | | cifar10_resnet18_AdvNegGrad_4.pth | Deer | 0.0 (27.805) | 91.19 (0.561) | |
| | | cifar10_resnet18_AdvNegGrad_5.pth | Dog | 0.0 (28.574) | 92.81 (0.456) | |
| | | cifar10_resnet18_AdvNegGrad_6.pth | Frog | 0.0 (28.360) | 92.18 (0.486) | |
| | | cifar10_resnet18_AdvNegGrad_7.pth | Horse | 0.0 (32.505) | 92.89 (0.401) | |
| | | cifar10_resnet18_AdvNegGrad_8.pth | Ship | 0.0 (29.307) | 91.34 (0.543) | |
| | | cifar10_resnet18_AdvNegGrad_9.pth | Truck | 0.0 (28.959) | 92.47 (0.474) | |
| | |
| | |
| | |
| | --- |
| | |
| | |
| | |
