  Deep neural networks are behind many of the recent successes in machine
learning applications. However, these models can produce overconfident
decisions while encountering out-of-distribution (OOD) examples or making a
wrong prediction. This inconsistent predictive confidence limits the
integration of independently-trained learning models into a larger system. This
paper introduces separable concept learning framework to realistically measure
the performance of classifiers in presence of OOD examples. In this setup,
several instances of a classifier are trained on different parts of a partition
of the set of classes. Later, the performance of the combination of these
models is evaluated on a separate test set. Unlike current OOD detection
techniques, this framework does not require auxiliary OOD datasets and does not
separate classification from detection performance. Furthermore, we present a
new strong baseline for more consistent predictive confidence in deep models,
called fitted ensembles, where overconfident predictions are rectified by
transformed versions of the original classification task. Fitted ensembles can
naturally detect OOD examples without requiring auxiliary data by observing
contradicting predictions among its components. Experiments on MNIST, SVHN,
CIFAR-10/100, and ImageNet show fitted ensemble significantly outperform
conventional ensembles on OOD examples and are possible to scale.
