| --- |
| license: mit |
| datasets: |
| - uoft-cs/cifar10 |
| language: |
| - en |
| metrics: |
| - accuracy, loss |
| base_model: |
| - jaeunglee/resnet18-cifar10-unlearning |
| tags: |
| - machine_unlearning |
| --- |
| |
| # Evaluation Report |
|
|
| ## Testing Data |
| **Dataset**: CIFAR-10 Test Set |
| **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 |
| - **Base Model**: ResNet18 |
| - **Dataset**: CIFAR-10 |
| - **Excluded Class**: Varies by model |
| - **Loss Function**: Negative Log-Likelihood Loss |
| - **Optimizer**: SGD with: |
| - Learning rate: 0.1 |
| - Momentum: 0.9 |
| - Weight decay: 5e-4 |
| - Nesterov: True |
| - **Training Epochs**: 62 |
| - **Batch Size**: 64 |
| - **Hardware**: Single GPU (NVIDIA GeForce RTX 3090) |
| - **Number of Retrain**: 1 |
|
|
| ### Algorithm |
| The **CF-k** algorithm was used for inexact unlearning. This method systematically removes the influence of a specific class from the model while retaining the ability to classify the remaining classes. Each resulting model (`cifar10_resnet18_CF-k_X.pth`) corresponds to a scenario where a single class (`X`) has been unlearned. The CF-k algorithm provides an efficient framework for evaluating the robustness and adaptability of models under inexact unlearning constraints. |
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| For more details on the CF-k algorithm, refer to the [GitHub repository](https://github.com/shash42/Evaluating-Inexact-Unlearning). |
|
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| --- |
|
|
| ## Results |
|
|
| | Model | Forget Class | Forget class acc(loss) | Retain class acc(loss) | |
| |--------------------------------|--------------|-------------------------|-------------------------| |
| | cifar10_resnet18_CF-k_0.pth | Airplane | 0.0 (4.659) | 95.49 (0.168) | |
| | cifar10_resnet18_CF-k_1.pth | Automobile | 0.0 (4.571) | 95.34 (0.181) | |
| | cifar10_resnet18_CF-k_2.pth | Bird | 0.0 (4.879) | 95.89 (0.158) | |
| | cifar10_resnet18_CF-k_3.pth | Cat | 0.0 (5.165) | 96.56 (0.127) | |
| | cifar10_resnet18_CF-k_4.pth | Deer | 0.0 (4.562) | 95.52 (0.170) | |
| | cifar10_resnet18_CF-k_5.pth | Dog | 0.0 (4.862) | 96.30 (0.137) | |
| | cifar10_resnet18_CF-k_6.pth | Frog | 0.0 (4.458) | 95.37 (0.185) | |
| | cifar10_resnet18_CF-k_7.pth | Horse | 0.0 (4.514) | 95.23 (0.179) | |
| | cifar10_resnet18_CF-k_8.pth | Ship | 0.0 (4.577) | 95.38 (0.178) | |
| | cifar10_resnet18_CF-k_9.pth | Truck | 0.0 (4.644) | 95.53 (0.174) | |
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