Machine Unlearning on CIFAR-100 (10% random data removal) benchmark leaderboard · SOTA2 Research

Machine Unlearning on CIFAR-100 (10% random data removal)

99.98Retain Accuracy (RA)Retrain

Evaluation Results

Method	Links
Retrain2025.09		99.98	24.26	75.56	48.44	0	27.37
FT2025.09		99.97	16.39	76.75	44.06	3.36	7.22
NoT2025.09		99.97	17.99	76.27	44.28	2.79	7.22
CoUn2025.09		99.97	22.01	72.88	47.82	1.39	9.63
Retrain2025.09		99.97	38.73	61.89	61.75	0	86.83
NegGrad+2025.09		99.96	30.09	75.46	47.72	1.67	7.62
l1-sparse2025.09		99.95	23.94	74.95	42.81	1.65	7.22
CoUn2025.09		99.91	36.81	56.49	53.92	3.8	19.29
NoT2025.09		99.89	20.29	61.82	43.55	9.2	8.68
NegGrad+2025.09		99.88	45.26	59.33	55	3.98	11.58
CoUn2025.09		99.82	32.37	63.8	39.64	1.31	5.71
FT2025.09		99.78	10.83	61.12	31.5	14.78	5.79
Retrain2025.09		99.75	33.23	67.07	40.69	0	15.58
l1-sparse2025.09		99.32	31.71	63.33	46.49	6.09	14.47
l1-sparse2025.09		99.27	26.96	68.01	35.31	3.27	3.42
FT2025.09		99.26	26.02	68.42	35.51	3.56	3.42
SalUn2025.09		99.18	38.01	54.78	69.24	4.03	5.1
SalUn2025.09		98.55	20.35	72.02	52.37	2.95	5.69
NoT2025.09		96.17	30.11	66.75	36.47	2.81	4.28
NegGrad+2025.09		94.92	35.44	65.54	40.67	2.15	3.42
SalUn2025.09		92.65	33	64.04	42.85	3.13	2.79