  Building neural network classifiers with an ability to distinguish between in
and out-of distribution inputs is an important step towards faithful deep
learning systems. Some of the successful approaches for this, resort to
architectural novelties, such as ensembles, with increased complexities in
terms of the number of parameters and training procedures. Whereas some other
approaches make use of surrogate samples, which are easy to create and work as
proxies for actual out-of-distribution (OOD) samples, to train the networks for
OOD detection. In this paper, we propose a very simple approach for enhancing
the ability of a pretrained network to detect OOD inputs without even altering
the original parameter values. We define a probabilistic trust interval for
each weight parameter of the network and optimize its size according to the
in-distribution (ID) inputs. It allows the network to sample additional weight
values along with the original values at the time of inference and use the
observed disagreement among the corresponding outputs for OOD detection. In
order to capture the disagreement effectively, we also propose a measure and
establish its suitability using empirical evidence. Our approach outperforms
the existing state-of-the-art methods on various OOD datasets by considerable
margins without using any real or surrogate OOD samples. We also analyze the
performance of our approach on adversarial and corrupted inputs such as
CIFAR-10-C and demonstrate its ability to clearly distinguish such inputs as
well. By using fundamental theorem of calculus on neural networks, we explain
why our technique doesn't need to observe OOD samples during training to
achieve results better than the previous works.
