---
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)          |



---