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Image Classification on CIFAR-100N ResNet-18 (test)

79.3Clean AccuracyInfoBatch + Ours

Evaluation results

Method	Links
InfoBatch + Ours2026.04		79.3	59.4	71.8	66	41.8	72.6	68	2.7
SeTa + Ours2026.04		79.3	56.3	70.8	60.5	41.6	71.9	64.3	0.7
InfoBatch2026.04		79	56.1	71.4	59.7	41.8	71.9	64.2	0.6
SeTa2026.04		79	55.6	70.2	59	41.6	71.4	63.2	0
InfoBatch + Ours2026.04		78.5	60.7	71.6	62	42.6	72.6	68.6	2.4
SeTa + Ours2026.04		78.4	56.3	71.2	61	41.9	72.2	66	1
Full-training2026.04		78.2	56.1	71.4	58.6	39.8	72.4	63.3	—
DUAL2026.04		78.1	58.8	71.3	55.4	22.7	72.2	60.8	-2.9
SeTa + Ours2026.04		77.8	55.5	72.3	63.3	42.7	72.7	67.6	1.7
InfoBatch + Ours2026.04		77.5	58.3	72.2	64.7	42.8	72.5	69.1	2.5
Dynamic Random2026.04		77.3	54.7	69.9	58.8	40.1	71.5	63.2	-0.6
Dyn-Unc2026.04		77.2	58.5	70.9	55.4	24.3	71.8	62.6	-2.7
DivBS2026.04		77.1	54.6	63.5	51	20.8	65.9	56.2	-7.2
IES2026.04		76.1	53.9	64.2	51	37	65.3	53.8	-5.5
Margin2026.04		75.4	48	59.2	15.9	6.3	57.3	42.1	-19.4
SSP2026.04		74.9	53	60.3	35.8	7.9	62.9	50.4	-13.5
Forget2026.04		74.8	57.7	70.2	51.3	12.5	68.4	59.2	-6.5
Glister2026.04		73.9	51.5	59.9	40.4	16.4	62.9	49.7	-12.2
Static Random2026.04		73.8	53.3	67.9	51.1	30.3	68.4	57.8	-5.3
Moderate2026.04		73.2	52.1	60.2	30.2	9.9	60	45.7	-15.5
Prune4ReL†2026.04		73.2	52.3	61.2	37	9	61.1	48.9	-13.8
GraNd2026.04		73	40.2	49.4	9.3	6.2	51.8	30.3	-25.6
Static Random2026.04		72.1	51.8	64	47.2	22.9	65.3	54.6	-8.8
SmallL2026.04		71.3	58	69.2	51.9	13.7	68.2	57	-7.2