  Conventional out-of-distribution (OOD) detection schemes based on variational
autoencoder or Random Network Distillation (RND) have been observed to assign
lower uncertainty to the OOD than the target distribution. In this work, we
discover that such conventional novelty detection schemes are also vulnerable
to the blurred images. Based on the observation, we construct a novel RND-based
OOD detector, SVD-RND, that utilizes blurred images during training. Our
detector is simple, efficient at test time, and outperforms baseline OOD
detectors in various domains. Further results show that SVD-RND learns better
target distribution representation than the baseline RND algorithm. Finally,
SVD-RND combined with geometric transform achieves near-perfect detection
accuracy on the CelebA dataset.
