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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**: 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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- **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**: 20 |
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- **Batch Size**: 256 |
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- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090) |
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### Selective Synapse Dampening Specifics |
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- **Lambda**: 1.0 |
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- **Alpha**: 10.0 |
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### Algorithm |
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The **SSD (Selective Synapse Dampening)** algorithm was used for inexact unlearning. This method selectively reduces the impact of a specific class on the model while preserving the performance on the remaining classes. |
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Each resulting model (`cifar10_resnet18_SSD_X.pth`) corresponds to a scenario where a single class (`X`) has been unlearned. SSD efficiently removes class-specific knowledge while maintaining robustness and generalizability. |
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For more details on the SSD algorithm, refer to the [GitHub repository](https://github.com/if-loops/selective-synaptic-dampening). |
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--- |
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## Results |
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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_SSD_0.pth | Airplane | 0.0 (8.102) | 83.38 (0.527) | |
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| cifar10_resnet18_SSD_1.pth | Automobile | 0.0 (6.550) | 94.62 (0.189) | |
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| cifar10_resnet18_SSD_2.pth | Bird | 0.0 (9.854) | 90.06 (0.328) | |
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| cifar10_resnet18_SSD_3.pth | Cat | 0.0 (8.428) | 90.00 (0.317) | |
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| cifar10_resnet18_SSD_4.pth | Deer | 0.0 (5.885) | 95.26 (0.161) | |
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| cifar10_resnet18_SSD_5.pth | Dog | 0.0 (6.917) | 12.53 (2.799) | |
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| cifar10_resnet18_SSD_6.pth | Frog | 0.0 (5.532) | 95.29 (0.156) | |
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| cifar10_resnet18_SSD_7.pth | Horse | 0.0 (7.328) | 17.71 (3.478) | |
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| cifar10_resnet18_SSD_8.pth | Ship | 0.0 (3.783) | 95.41 (0.158) | |
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| cifar10_resnet18_SSD_9.pth | Truck | 0.0 (5.864) | 94.29 (0.198) | |
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