  We present a new methodology for detecting out-of-distribution (OOD) images
by utilizing norms of the score estimates at multiple noise scales. A score is
defined to be the gradient of the log density with respect to the input data.
Our methodology is completely unsupervised and follows a straight forward
training scheme. First, we train a deep network to estimate scores for levels
of noise. Once trained, we calculate the noisy score estimates for N
in-distribution samples and take the L2-norms across the input dimensions
(resulting in an NxL matrix). Then we train an auxiliary model (such as a
Gaussian Mixture Model) to learn the in-distribution spatial regions in this
L-dimensional space. This auxiliary model can now be used to identify points
that reside outside the learned space. Despite its simplicity, our experiments
show that this methodology significantly outperforms the state-of-the-art in
detecting out-of-distribution images. For example, our method can effectively
separate CIFAR-10 (inlier) and SVHN (OOD) images, a setting which has been
previously shown to be difficult for deep likelihood models.
