  Overconfident predictions on out-of-distribution (OOD) samples is a thorny
issue for deep neural networks. The key to resolve the OOD overconfidence issue
inherently is to build a subset of OOD samples and then suppress predictions on
them. This paper proposes the Chamfer OOD examples (CODEs), whose distribution
is close to that of in-distribution samples, and thus could be utilized to
alleviate the OOD overconfidence issue effectively by suppressing predictions
on them. To obtain CODEs, we first generate seed OOD examples via
slicing&splicing operations on in-distribution samples from different
categories, and then feed them to the Chamfer generative adversarial network
for distribution transformation, without accessing to any extra data. Training
with suppressing predictions on CODEs is validated to alleviate the OOD
overconfidence issue largely without hurting classification accuracy, and
outperform the state-of-the-art methods. Besides, we demonstrate CODEs are
useful for improving OOD detection and classification.
