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Yurim0507/
SALUN

Name: SALUN
Availability: InStock

machine_unlearning

uoft-cs/cifar10

jaeunglee/resnet18-cifar10-unlearning

finetune

mit

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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)

Yurim0507/SALUN