metadata
license: mit
datasets:
- uoft-cs/cifar10
language:
- en
metrics:
- accuracy
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)
Training Details
Training Procedure
- Base Model: ResNet18
- Dataset: CIFAR-10
- Excluded Class: Varies by model
- Loss Function: Negative Log-Likelihood Loss
- Optimizer: SGD with:
- Learning rate: 0.01
- Momentum: 0.9
- Weight decay: 5e-4
- Nesterov: True
- Training Epochs: 20
- Batch Size: 256
- Hardware: Single GPU (NVIDIA GeForce RTX 3090)
Selective Synapse Dampening Specifics
- Lambda: 1.0
- Alpha: 10.0
Algorithm
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.
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.
For more details on the SSD algorithm, refer to the GitHub repository.
Results
| Model | Forget Class | Forget class acc(loss) | Retain class acc(loss) |
|---|---|---|---|
| cifar10_resnet18_SSD_0.pth | Airplane | 0.0 (8.102) | 83.38 (0.527) |
| cifar10_resnet18_SSD_1.pth | Automobile | 0.0 (6.550) | 94.62 (0.189) |
| cifar10_resnet18_SSD_2.pth | Bird | 0.0 (9.854) | 90.06 (0.328) |
| cifar10_resnet18_SSD_3.pth | Cat | 0.0 (8.428) | 90.00 (0.317) |
| cifar10_resnet18_SSD_4.pth | Deer | 0.0 (5.885) | 95.26 (0.161) |
| cifar10_resnet18_SSD_5.pth | Dog | 0.0 (6.917) | 12.53 (2.799) |
| cifar10_resnet18_SSD_6.pth | Frog | 0.0 (5.532) | 95.29 (0.156) |
| cifar10_resnet18_SSD_7.pth | Horse | 0.0 (7.328) | 17.71 (3.478) |
| cifar10_resnet18_SSD_8.pth | Ship | 0.0 (3.783) | 95.41 (0.158) |
| cifar10_resnet18_SSD_9.pth | Truck | 0.0 (5.864) | 94.29 (0.198) |