IC-1003Pretrained 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
The paper formulates neuron activation states as the product of raw neuron outputs and KL-divergence gradients, then measures the coverage of these states under in-distribution training data. On pretrained ResNet-50 and ViT-B/16 (both trained on ImageNet-1k), out-of-distribution inputs trigger neuron states that fall outside the expected coverage area, producing lower coverage scores than in-distribution inputs. This separability in the models' internal neuron behavior allows a post-hoc uncertainty score (NAC-UE) to distinguish IN from OOD samples without modifying the model weights, achieving state-of-the-art AUROC across three OOD datasets.
The separability is measured using the paper's specific neuron state formulation (z ⊙ ∂dkl/∂z); raw neuron outputs alone show less separation (FPR95 35.72% vs 16.58% on layer4 of ResNet-50). The method requires a small subset of training data (1,000–50,000 images) to build the coverage model.