  Deep neural networks for image classification only learn to map
in-distribution inputs to their corresponding ground truth labels in training
without differentiating out-of-distribution samples from in-distribution ones.
This results from the assumption that all samples are independent and
identically distributed (IID) without distributional distinction. Therefore, a
pretrained network learned from in-distribution samples treats
out-of-distribution samples as in-distribution and makes high-confidence
predictions on them in the test phase. To address this issue, we draw
out-of-distribution samples from the vicinity distribution of training
in-distribution samples for learning to reject the prediction on
out-of-distribution inputs. A \textit{Cross-class Vicinity Distribution} is
introduced by assuming that an out-of-distribution sample generated by mixing
multiple in-distribution samples does not share the same classes of its
constituents. We thus improve the discriminability of a pretrained network by
finetuning it with out-of-distribution samples drawn from the cross-class
vicinity distribution, where each out-of-distribution input corresponds to a
complementary label. Experiments on various in-/out-of-distribution datasets
show that the proposed method significantly outperforms the existing methods in
improving the capacity of discriminating between in- and out-of-distribution
samples.
