  We study out-of-distribution (OOD) prediction behavior of neural networks
when they classify images from unseen classes or corrupted images. To probe the
OOD behavior, we introduce a new measure, nearest category generalization
(NCG), where we compute the fraction of OOD inputs that are classified with the
same label as their nearest neighbor in the training set. Our motivation stems
from understanding the prediction patterns of adversarially robust networks,
since previous work has identified unexpected consequences of training to be
robust to norm-bounded perturbations. We find that robust networks have
consistently higher NCG accuracy than natural training, even when the OOD data
is much farther away than the robustness radius. This implies that the local
regularization of robust training has a significant impact on the network's
decision regions. We replicate our findings using many datasets, comparing new
and existing training methods. Overall, adversarially robust networks resemble
a nearest neighbor classifier when it comes to OOD data. Code available at
https://github.com/yangarbiter/nearest-category-generalization.
