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Machine Unlearning on CIFAR10 N=500 (RANDOM)

0KL Divergence (Train)pθr
-0.02160.12420.270.4158May 11, 2026

Evaluation results

MethodLinks
pθrArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050—089.790100
pθrModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050—096.997.2100
pθModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.01—0.1296.9100—
LDAModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.020.020.0996.81001.3
LDA-MixModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.020.020.0596.898.71.6
LDA-2CModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.020.020.0996.899.21.3
DIR-2CModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.020.020.196.81001.3
SCRUBModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.020.020.1196.899.40.66
DIRModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.030.040.196.499.51.2
SalUnModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.030.90.0996.51000.49
RL+FTModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.030.240.1196.51000.64
GA+FTModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.030.030.0596.799.119
FTModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.030.030.0896.799.719
GAModel Architecture=dinov2-mlp1, Scenario=RANDOM, Forget Set Size (N)=5002026.050.03270.0396.797.90.21
DIRArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.410.40.7688.990.64.7
pθArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.42—0.9589.6100—
LDAArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.420.430.9489.51007
LDA-MixArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.420.420.8889.51007
LDA-2CArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.420.430.9489.699.96.9
DIR-2CArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.420.420.8789.51004.6
SCRUBArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.420.460.9289.599.61.7
GAArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.420.620.9189.599.50.48
RL+FTArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.430.470.8389.197.70.79
SalUnArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.440.640.8388.996.51.2
GA+FTArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.520.661.189.498.970
FTArchitecture=resnet18, Scenario=RANDOM, Number of forgotten samples (N)=5002026.050.540.671.189.510070