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Image Classification on CIFAR-10N (Robustness Metrics)

95.6Accuracy (Clean)Full-training
89.98491.44292.994.358Apr 8, 2026

Evaluation results

MethodLinks
Full-trainingBackbone=ResNet-18, Pruning Method=None2026.0495.690.778.391.385.465.690.685.8—
InfoBatchBackbone=ResNet-18, Prune Ratio=30%2026.0495.39178.19285.667.591.3870.5
InfoBatch + AlignPruneBackbone=ResNet-18, Prune Ratio=30%2026.0495.391.48292.787.967.892.9902.1
SeTa + AlignPruneBackbone=ResNet-18, Prune Ratio=30%2026.0495.390.779.192.58564.691.889.30.6
SeTaBackbone=ResNet-18, Prune Ratio=30%2026.0495.290.676.391.884.764.290.888.4-0.2
InfoBatchBackbone=ResNet-18, Prune Ratio=50%2026.049590.577.79287.564.39287.50.4
InfoBatch + AlignPruneBackbone=ResNet-18, Prune Ratio=50%2026.049591.382.492.887.664.592.189.81.5
SeTa + AlignPruneBackbone=ResNet-18, Prune Ratio=50%2026.0494.989.779.391.987.660.292.288.50.1
Dynamic RandomBackbone=ResNet-18, Prune Ratio=30%2026.0494.890.377.390.884.264.891.584.5-0.6
SeTaBackbone=ResNet-18, Prune Ratio=50%2026.0494.889.478.591.3865391.787.9-1.4
Static RandomBackbone=ResNet-18, Prune Ratio=30%2026.0494.689.9758981.257.687.982.5-3.2
Dynamic RandomBackbone=ResNet-18, Prune Ratio=50%2026.0494.589.276.188.983.763.390.683.7-1.7
SeTa + AlignPruneBackbone=ResNet-18, Prune Ratio=70%2026.0494.59079.492.687.75892.588.50
InfoBatchBackbone=ResNet-18, Prune Ratio=70%2026.0494.389.979.492.4876192.588.30.2
InfoBatch + AlignPruneBackbone=ResNet-18, Prune Ratio=70%2026.0494.390.88192.887.961.192.989.20.8
SeTaBackbone=ResNet-18, Prune Ratio=70%2026.0494.389.577.792.287.130.492.288.1-4
Static RandomBackbone=ResNet-18, Prune Ratio=50%2026.0493.388.672.487.477.551.485.877.9-6.1
Dynamic RandomBackbone=ResNet-18, Prune Ratio=70%2026.049387.475.987.484.660.291.486.8-2.1
Static RandomBackbone=ResNet-18, Prune Ratio=70%2026.0490.286.66985.373.841.482.375.3-9.9