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: Top-1 Accuracy
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: 8
- Batch Size: 512
- Hardware: Single GPU (NVIDIA GeForce RTX 3090)
SALUN Specifics
- Threshold: 0.1
Algorithm
The SALUN (Saliency Unlearning) algorithm was used for inexact unlearning. This method involves impairing the influence of a specific class and fine-tuning the model to regain its accuracy for the remaining classes.
Each resulting model (cifar10_resnet18_SALUN_X.pth) corresponds to a scenario where a single class (X) has been unlearned. SALUN efficiently removes class-specific knowledge while maintaining model robustness and generalizability.
For more details on the SALUN algorithm, refer to the GitHub repository.
Results
| Model | Excluded Class | Forget class acc(loss) | Retain class acc(loss) |
|---|---|---|---|
| cifar10_resnet18_SALUN_0.pth | Airplane | 0.7 (2.550) | 90.00 (0.347) |
| cifar10_resnet18_SALUN_1.pth | Automobile | 0.0 (2.976) | 88.73 (0.404) |
| cifar10_resnet18_SALUN_2.pth | Bird | 2.4 (2.862) | 89.13 (0.356) |
| cifar10_resnet18_SALUN_3.pth | Cat | 0.0 (3.640) | 92.13 (0.262) |
| cifar10_resnet18_SALUN_4.pth | Deer | 0.9 (2.749) | 89.74 (0.349) |
| cifar10_resnet18_SALUN_5.pth | Dog | 3.7 (2.870) | 88.92 (0.363) |
| cifar10_resnet18_SALUN_6.pth | Frog | 1.7 (3.236) | 86.23 (0.486) |
| cifar10_resnet18_SALUN_7.pth | Horse | 0.0 (3.119) | 90.03 (0.342) |
| cifar10_resnet18_SALUN_8.pth | Ship | 9.4 (2.685) | 90.86 (0.320) |
| cifar10_resnet18_SALUN_9.pth | Truck | 0.5 (2.748) | 89.88 (0.362) |