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IC-028
SPADE, an abstaining classifier built on top of ResNet, ViT, and VGG models, detects out-of-distribution and adversarial samples with provable guarantees.
[eval]
IC-1003
Pretrained ResNet-50 and ViT-B/16 exhibit neuron activation patterns that are separable between in-distribution and out-of-distribution inputs, enabling post-hoc OOD detection without model modification
[eval]
IC-990
ResNet-50 and DenseNet-101 exhibit a higher mean-to-variance ratio in penultimate pre-ReLU activations for in-distribution samples than for out-of-distribution samples, and the activation-based scaling factor is well-separated between ID and OOD
[eval]