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Image Classification on TinyImageNet D (test)

57AccuracySCRUB
44.10447.45250.854.148Apr 10, 2026
Updated 7mo ago

Evaluation results

MethodLinks
SCRUBUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.04572.4
FTUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.0456.21.5
SCRUBUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0455.66.1
RetrainingUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.0454.60
IEU w/GAUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.0454.10.5
IEU w/NoisyUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.04540.6
FTUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0453.84.3
IEU w/GA+NoisyUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.0453.61
RLUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.0452.22.4
RLUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0450.71.3
RetrainingUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0449.50
IEU w/GA+NoisyUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0449.50
IEU w/GAUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0449.40.1
IEU w/NoisyUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0449.40
SALUNUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=30%, Backbone=ResNet2026.0448.66
SALUNUnlearning Scenario=Random Data Forgetting, Forgetting Ratio=50%, Backbone=ResNet2026.0444.64.9