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---
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) |
---
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